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Terence Soule

University of Idaho, Computer Science, Faculty Member

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Dr. Soule is a Professor of Computer Science at the University of Idaho. His research is in evolutionary computation, evolutionary robotics, and evolution in video games. He is the author of "A Project Based Introduction to C++" published by KendallHunt.

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University of California, San Diego

University of Texas at El Paso (UTEP)

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Nicholas R Micinski

University of Maine

Ernestina Coast

London School of Economics and Political Science

University of Birmingham

Jonathan Xavier Inda

University of Illinois at Chicago

Roshan Chitrakar

Nepal College of Information Technology

Graduate Center of the City University of New York

Stanford University

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Papers by Terence Soule

Darwins_Demons.pdf

Darwin's Demons is a video game that incorporates evolutionary biology. It features a populat... more Darwin's Demons is a video game that incorporates evolutionary biology. It features a population of enemies encoded by diploid digital genomes of quantitative trait loci. Each round of gameplay is modeled as a generation in which a new enemy population is produced using tournament selection with replacement. Each parent provides a chromosome produced with free recombination. Mutations are then drawn from a Gaussian distribution. Enemies gain fitness by shooting close to the player and spending time near the bottom of the screen. These conditions produce rapid rates of adaptation to player strategies. Ultimately, Darwin's Demons allows players to observe (and sometimes exploit) evolution through the course of gameplay.<br>

In the wild, groups of hyenas are often observed to cooperate in driving lions away from a kill i... more In the wild, groups of hyenas are often observed to cooperate in driving lions away from a kill in order to claim it for them-selves. Because lions are much larger and can easily injure or kill a single hyena, this is a difficult, high risk/high reward behavior requiring complex cooperation by the hyenas. The hyenas depend on communication to coordinate their attack. In this paper, we attempt to evolve hyena behaviors that successfully drive-off simulated lions. We are particularly interested in how the type of communication influences the evolution of successful strategies. Several forms of communi-cation are tested including two inspired by hyena behavior. The first is a generalized vocalization or “call ” that can be either local or global and used by any hyena. The second mimics the recognition of a special hyena, which we refer to as the “flag-bearer”. Our results show that the presence of a flag-bearer leads to the evolution of significantly more ef-fective coordination than e...

R.B.: An analysis of the causes of code growth in genetic programming

Abstract. This research examines the cause of code growth (bloat) in genetic programming (GP). Cu... more Abstract. This research examines the cause of code growth (bloat) in genetic programming (GP). Currently there are three hypothesized causes of code growth in GP: protection, drift, and removal bias. We show that single node mutations increase code growth in evolving programs. This is strong evidence that the protective hypothesis is correct. We also show a negative correlation between the size of the branch removed during crossover and the resulting change in fitness, but a much weaker correlation for added branches. These results support the removal bias hypothesis, but seem to refute the drift hypothesis. Our results also suggest that there are serious disadvantages to the tree structured programs commonly evolved with GP, because the nodes near the root are effectively fixed in the very early generations. Keywords: genetic programming, code growth, code bloat, crossover

Darwin’s Demons: Does Evolution Improve the Game?

Applications of Evolutionary Computation, 2017

It is widely assumed that evolution has the potential to make better video games. However, relati... more It is widely assumed that evolution has the potential to make better video games. However, relatively few commercial games have been released that use evolution as a core game mechanic, and of these games only a very small sub-set have shown that evolution occurs as expected and improves game play as intended. Thus, there remains a critical gap between studies showing the clear potential of evolution to improve video games and studies showing that evolution did improve game play in a commercially released game.

Learned Anticipation Strategy on Complex Behaviors and as an Approach to Generalization Behavior for the Coordination of an AUV Fleet

OCEANS 2018 MTS/IEEE Charleston, 2018

Anticipation is natural behavior seen in certain organisms when they try to adapt their behavior ... more Anticipation is natural behavior seen in certain organisms when they try to adapt their behavior to create an accurate reaction to an event that will happen in the future based on sequential information that extracted from the environment and other organisms. Anticipation has been studied from both the human and the animal perspectives and science has been taking some of these ideas to implement this behavior in machines. Research and studies of anticipation using Artificial Intelligence (AI) methods has been growing during the past years within different fields, including Robotics, Stock Market, Weather Forecast, Social Media, among others. At the University of Idaho, we have been focusing on studying Anticipation using Autonomous Underwater Vehicles (AUV). This paper analyzes the impact on coordination of including an anticipation module to adapt to complex behaviors and to make decisions based on generalization while running as part of a simulation of a fleet of AUVs following a magnetic signature assessment (MSA) task. During this task, a moving Target Ship (TS) shares information about its behavior with the fleet of AUVs while the fleet tries to maintain formation and reach a designated meeting point at the same time as the TS. The TS behaviors include sudden and/or complex changes in movement during the experiment. These changes in the TS’s movement make it difficult for the AUVs to reach the meeting point at the same time as the TS without additional information. Progress messages from the TS are used by the AUVs to adjust their behavior, but the messages may be sparse. A portion of the data that represents the TS’s behavior was used as training data for the anticipation module in the AUVs. The remaining data was used as brand new cases that the anticipation module would try to solve to encourage generalization. Generalization occurs when the robot can solve a task that it was not trained for, but there was sufficient training data to result in reasonable solutions. For these experiments, the messages that are sent by the TS to the AUVs report the TS’s progress to the goal. The TS reports its progress using Fuzzy set membership values. This strategy is used to model real-world situations where progress cannot be defined with exact values. The membership values are represented by fuzzy sets including On Schedule, Behind Schedule, Ahead of Schedule, etc. This strategy gives a more human-like approach to the AUVs for decision making because humans often say things like “I’m a little behind schedule”. The anticipation module works as an aid for the communication between the TS and the fleet of AUVs. It tries to keep the fleet running on track and synchronized with the TS behavior by filling in missing messages with an anticipated message. In this research it is assumed that missing and corrupt messages occur because of a noisy or low-bandwidth communications. The experiments simulate this condition by sending messages more infrequently. The TS stops broadcasting messages for a specific time period that has been selected before starting the test. Versions of the anticipation module, using a Neural Network model and a Fuzzy Logic model were tested. To have comparable results, the position of the meeting point is constant during each experiment and is used as a point of reference. The effectiveness of the anticipation module was evaluated by measuring the distance between the actual meeting point of the fleet of AUVs and the TS and the desired meeting point. In general the anticipation module reduced the error significantly. This included the tests using the novel behaviors that were not part of the training set. These last results lead to the idea that, with the right training data, the AUVs are displaying generalization behavior. In general, the anticipation module helps the fleet of AUVs to infer the behavior of the TS and they synchronize to the TS even when there was a lack of information, caused by missing messages.

Genetic Programming Theory and Practice V

Genetic and Evolutionary Computation Series, 2008

, except for brief excerpts in connection with reviews or scholarly analysis. Use in connection w... more , except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.

Single feature polymorphisms: detection and influence on short-oligonucleotide microarrays

ABSTRACT Gene expression microarrays are a tool used by ecologists, to study the genome-wide tran... more ABSTRACT Gene expression microarrays are a tool used by ecologists, to study the genome-wide transcriptional variation that underlies complex interactions among and between organisms and their environment. Microarrays allow for the simultaneous measurement of thousands of gene products across many samples. These measurements rely on sequence similarity for efficient hybridization of sample mRNA targets to the microarray probes. Genetic polymorphisms located within a microarray’s probe affects this hybridization efficiency. These differences in probe level hybridization efficiencies are known as single feature polymorphisms (SFPs). SFPs are both a source of error, they negatively impact the estimate for transcript abundance, and a source of information, they are genetic markers. The Affymetrix⃝R 3’ IVT microarray platform uses 11 to 16 short-oligonucleotide probes, covering different regions of a gene, to measure transcript abundance. The redundancy of probes within the same gene, allows for the capability to detect candidate SFPs from the data itself. This dissertation presents three new results pertaining to SFPs in Affymetrix⃝R gene expression microarray data. The first result is a novel, improved algorithm for the accurate detection of SFPs. The second result evaluates the impact of SFPs to false positives in both differential expression analysis and the detection of SFPs themselves and offers a solution to reduce false positives induced by SFPs. Finally, the third result demonstrates how SFPs can be used to provide a new means for evaluating the accuracy of critical microarray preprocessing steps. These results will provide ecologists with new tools and techniques when using Affymetrix⃝R 3’ IVT microarrays in ecological microarray studies.

MiCA: web-based computational tools for the analysis of microbial community structure and composition based on T-RFLP of 16S rRNA genes

Proceedings. IEEE Computer Society Bioinformatics Conference, 2002

Analyses of microbial community structure based on terminal restriction fragment length polymorph... more Analyses of microbial community structure based on terminal restriction fragment length polymorphisms (T-RFLP) of 16S rRNA genes are hindered by the lack of computational tools needed to aid experimental design, and to archive and analyze large data sets. The aim of this research was to develop a suite of Web-based tools that would enable researchers to perform several tasks, including:

Quantum Genetic Algorithms

Recent developments in quantum technology have shown that quantum computers can provide a dramati... more Recent developments in quantum technology have shown that quantum computers can provide a dramatic advantage over classical computers for some algorithms. Since most problems of real interest for genetic algorithms (GAs) have a vast search space [Holland, 1975], it seems appropriate to consider how quantum parallelism can be applied to GAs. In this paper we present a simple quantum approach to genetic algorithms and analyze its benefits and drawbacks. This is significant because to date there are only a handful of quantum algorithms [Williams and Clearwater, 1997].

A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams

Big Data and Cognitive Computing

Outlier detection is a statistical procedure that aims to find suspicious events or items that ar... more Outlier detection is a statistical procedure that aims to find suspicious events or items that are different from the normal form of a dataset. It has drawn considerable interest in the field of data mining and machine learning. Outlier detection is important in many applications, including fraud detection in credit card transactions and network intrusion detection. There are two general types of outlier detection: global and local. Global outliers fall outside the normal range for an entire dataset, whereas local outliers may fall within the normal range for the entire dataset, but outside the normal range for the surrounding data points. This paper addresses local outlier detection. The best-known technique for local outlier detection is the Local Outlier Factor (LOF), a density-based technique. There are many LOF algorithms for a static data environment; however, these algorithms cannot be applied directly to data streams, which are an important type of big data. In general, loca...

Girls Build Excitement for Math from Scratch

Mathematics Teaching in the Middle School

To increase digital literacy and mathematical fluency, students electronically piece together det... more

Bee-Inspired Landmark Recognition in Robotic Navigation

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO '16 Companion, 2016

Research has shown that honeybees have a remarkable ability to use landmarks to travel miles to f... more Research has shown that honeybees have a remarkable ability to use landmarks to travel miles to find-and return to-a food source [1, 2]. The long-term goal of this research is to use physical robots, incorporating a variety of image processing approaches, to examine this ability. The results may also improve our ability to develop effective landmark guidance systems for robots. This research examines whether robots using a simple genetic algorithm (GA) and neural network (NN) that are trained to search for a target object adopt the use of landmarks to aid in the search-even when landmarks are not explicitly considered. This is a first step towards more complex experiments in less controlled environments. The simplified environment used in these experiments is controllable, making it easier to determine and understand the robot's behavior before moving to a more complex external environment. The robot uses a feed-forward, episodic NN for navigation-there is no inherent use of a memory bank of images. It is trained using Learning from Demonstration (LfD) to follow a simple search pattern independent of landmarks, but visual cues that could act as landmarks are present in the environment. Our results show that even under these conditions visual cues, i.e. landmarks, are incorporated in the learned search.

Stochastic training of a biologically plausible spino-neuromuscular system model

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, 2007

A primary goal of evolutionary robotics is to create systems that are as robust and adaptive as t... more A primary goal of evolutionary robotics is to create systems that are as robust and adaptive as the human body. Moving toward this goal often involves training control systems that process sensory information in a way similar to humans. Artificial neural networks have been an increasingly popular option for this because they consist of processing units that approximate the synaptic activity of biological signal processing units, i.e. neurons. In this paper we train a nonlinear recurrent spino-neuromuscular system (SNMS) model and compare the performance of genetic algorithms (GA)s, particle swarm optimizers (PSO)s, and GA/PSO hybrids. Several key features of the SNMS model have previously been modeled individually but have not been combined into a single model as is done here. The results show that each algorithm produces fit solutions and generates fundamental biological behaviors, such as tonic tension behaviors and triceps activation patterns, that are not explicitly trained.

Voting teams: a cooperative approach to non-typical problems using genetic programming

Gecco, 1999

... (There are 25 or 32 possible voting permutations. Only one of these has all members votingcor... more

Abstention Reduces Errors - decision Abstaining N-version Genetic Programming

Gecco, 2002

Limiting code growth in genetic programming

Resilient Individuals Improve Evolutionary Search

Alife, 2005

Results from the artificial life community show that under some conditions evolving populations c... more Results from the artificial life community show that under some conditions evolving populations converge on broader, but less fit peaks in the fitness landscape and avoid more fit, but narrower peaks. Results from the evolutionary computation community show that over time genotypes evolve to become more resilient, where resiliency (or genetic robustness) is defined as the ability of an individual to resist the potentially negative effects of genetic operations. This article demonstrates a previously unobserved evolutionary dynamic: in populations initially favoring a low, broad fitness peak, increases in resiliency result in the population shifting to a higher, narrower fitness peak. In these cases increasing resiliency is a necessary precondition for finding narrower peaks.

Redundant genes and the evolution of robustness

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, 2006

In this paper we demonstrate that pressure for robustness combined with function sets containing ... more

Genetic programming: parametric analysis of structure altering mutation techniques

Proceedings of the 2005 Workshops on Genetic and Evolutionary Computation, 2005

We hypothesize that the relationship between parameter settings, specically parameters controllin... more We hypothesize that the relationship between parameter settings, specically parameters controlling mutation, and performance is non-linear in genetic programs. Genetic programming environments have few means for a priori determination of appropriate parameters values. The hypothesized nonlinear behavior of genetic programming creates diculty in selecting parameter values for many problems. In this paper we study three structure altering mutation techniques using parametric analysis on a problem with scalable complexity. We nd through parameter analysis that two of the three mutation types tested exhibit nonlinear behavior. Higher mutation rates cause a larger degree of nonlinear behavior as measured by tness and computational eort. Characterization of the mutation techniques using parametric analysis conrms the nonlinear behavior. In addition, we propose an extension to the existing parameter setting taxonomy to include commonly used structure altering mutation attributes. Finally we show that the proportion of mutations applied to internal nodes, instead of leaf nodes, has a signicant eect on performance.

Genetic Programming Theory and Practice VI

Darwins_Demons.pdf

Darwin's Demons is a video game that incorporates evolutionary biology. It features a populat... more Darwin's Demons is a video game that incorporates evolutionary biology. It features a population of enemies encoded by diploid digital genomes of quantitative trait loci. Each round of gameplay is modeled as a generation in which a new enemy population is produced using tournament selection with replacement. Each parent provides a chromosome produced with free recombination. Mutations are then drawn from a Gaussian distribution. Enemies gain fitness by shooting close to the player and spending time near the bottom of the screen. These conditions produce rapid rates of adaptation to player strategies. Ultimately, Darwin's Demons allows players to observe (and sometimes exploit) evolution through the course of gameplay.<br>

In the wild, groups of hyenas are often observed to cooperate in driving lions away from a kill i... more In the wild, groups of hyenas are often observed to cooperate in driving lions away from a kill in order to claim it for them-selves. Because lions are much larger and can easily injure or kill a single hyena, this is a difficult, high risk/high reward behavior requiring complex cooperation by the hyenas. The hyenas depend on communication to coordinate their attack. In this paper, we attempt to evolve hyena behaviors that successfully drive-off simulated lions. We are particularly interested in how the type of communication influences the evolution of successful strategies. Several forms of communi-cation are tested including two inspired by hyena behavior. The first is a generalized vocalization or “call ” that can be either local or global and used by any hyena. The second mimics the recognition of a special hyena, which we refer to as the “flag-bearer”. Our results show that the presence of a flag-bearer leads to the evolution of significantly more ef-fective coordination than e...

R.B.: An analysis of the causes of code growth in genetic programming

Abstract. This research examines the cause of code growth (bloat) in genetic programming (GP). Cu... more Abstract. This research examines the cause of code growth (bloat) in genetic programming (GP). Currently there are three hypothesized causes of code growth in GP: protection, drift, and removal bias. We show that single node mutations increase code growth in evolving programs. This is strong evidence that the protective hypothesis is correct. We also show a negative correlation between the size of the branch removed during crossover and the resulting change in fitness, but a much weaker correlation for added branches. These results support the removal bias hypothesis, but seem to refute the drift hypothesis. Our results also suggest that there are serious disadvantages to the tree structured programs commonly evolved with GP, because the nodes near the root are effectively fixed in the very early generations. Keywords: genetic programming, code growth, code bloat, crossover

Darwin’s Demons: Does Evolution Improve the Game?

Applications of Evolutionary Computation, 2017

It is widely assumed that evolution has the potential to make better video games. However, relati... more It is widely assumed that evolution has the potential to make better video games. However, relatively few commercial games have been released that use evolution as a core game mechanic, and of these games only a very small sub-set have shown that evolution occurs as expected and improves game play as intended. Thus, there remains a critical gap between studies showing the clear potential of evolution to improve video games and studies showing that evolution did improve game play in a commercially released game.

Learned Anticipation Strategy on Complex Behaviors and as an Approach to Generalization Behavior for the Coordination of an AUV Fleet

OCEANS 2018 MTS/IEEE Charleston, 2018

Anticipation is natural behavior seen in certain organisms when they try to adapt their behavior ... more Anticipation is natural behavior seen in certain organisms when they try to adapt their behavior to create an accurate reaction to an event that will happen in the future based on sequential information that extracted from the environment and other organisms. Anticipation has been studied from both the human and the animal perspectives and science has been taking some of these ideas to implement this behavior in machines. Research and studies of anticipation using Artificial Intelligence (AI) methods has been growing during the past years within different fields, including Robotics, Stock Market, Weather Forecast, Social Media, among others. At the University of Idaho, we have been focusing on studying Anticipation using Autonomous Underwater Vehicles (AUV). This paper analyzes the impact on coordination of including an anticipation module to adapt to complex behaviors and to make decisions based on generalization while running as part of a simulation of a fleet of AUVs following a magnetic signature assessment (MSA) task. During this task, a moving Target Ship (TS) shares information about its behavior with the fleet of AUVs while the fleet tries to maintain formation and reach a designated meeting point at the same time as the TS. The TS behaviors include sudden and/or complex changes in movement during the experiment. These changes in the TS’s movement make it difficult for the AUVs to reach the meeting point at the same time as the TS without additional information. Progress messages from the TS are used by the AUVs to adjust their behavior, but the messages may be sparse. A portion of the data that represents the TS’s behavior was used as training data for the anticipation module in the AUVs. The remaining data was used as brand new cases that the anticipation module would try to solve to encourage generalization. Generalization occurs when the robot can solve a task that it was not trained for, but there was sufficient training data to result in reasonable solutions. For these experiments, the messages that are sent by the TS to the AUVs report the TS’s progress to the goal. The TS reports its progress using Fuzzy set membership values. This strategy is used to model real-world situations where progress cannot be defined with exact values. The membership values are represented by fuzzy sets including On Schedule, Behind Schedule, Ahead of Schedule, etc. This strategy gives a more human-like approach to the AUVs for decision making because humans often say things like “I’m a little behind schedule”. The anticipation module works as an aid for the communication between the TS and the fleet of AUVs. It tries to keep the fleet running on track and synchronized with the TS behavior by filling in missing messages with an anticipated message. In this research it is assumed that missing and corrupt messages occur because of a noisy or low-bandwidth communications. The experiments simulate this condition by sending messages more infrequently. The TS stops broadcasting messages for a specific time period that has been selected before starting the test. Versions of the anticipation module, using a Neural Network model and a Fuzzy Logic model were tested. To have comparable results, the position of the meeting point is constant during each experiment and is used as a point of reference. The effectiveness of the anticipation module was evaluated by measuring the distance between the actual meeting point of the fleet of AUVs and the TS and the desired meeting point. In general the anticipation module reduced the error significantly. This included the tests using the novel behaviors that were not part of the training set. These last results lead to the idea that, with the right training data, the AUVs are displaying generalization behavior. In general, the anticipation module helps the fleet of AUVs to infer the behavior of the TS and they synchronize to the TS even when there was a lack of information, caused by missing messages.

Genetic Programming Theory and Practice V

Genetic and Evolutionary Computation Series, 2008

, except for brief excerpts in connection with reviews or scholarly analysis. Use in connection w... more , except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.

Single feature polymorphisms: detection and influence on short-oligonucleotide microarrays

ABSTRACT Gene expression microarrays are a tool used by ecologists, to study the genome-wide tran... more ABSTRACT Gene expression microarrays are a tool used by ecologists, to study the genome-wide transcriptional variation that underlies complex interactions among and between organisms and their environment. Microarrays allow for the simultaneous measurement of thousands of gene products across many samples. These measurements rely on sequence similarity for efficient hybridization of sample mRNA targets to the microarray probes. Genetic polymorphisms located within a microarray’s probe affects this hybridization efficiency. These differences in probe level hybridization efficiencies are known as single feature polymorphisms (SFPs). SFPs are both a source of error, they negatively impact the estimate for transcript abundance, and a source of information, they are genetic markers. The Affymetrix⃝R 3’ IVT microarray platform uses 11 to 16 short-oligonucleotide probes, covering different regions of a gene, to measure transcript abundance. The redundancy of probes within the same gene, allows for the capability to detect candidate SFPs from the data itself. This dissertation presents three new results pertaining to SFPs in Affymetrix⃝R gene expression microarray data. The first result is a novel, improved algorithm for the accurate detection of SFPs. The second result evaluates the impact of SFPs to false positives in both differential expression analysis and the detection of SFPs themselves and offers a solution to reduce false positives induced by SFPs. Finally, the third result demonstrates how SFPs can be used to provide a new means for evaluating the accuracy of critical microarray preprocessing steps. These results will provide ecologists with new tools and techniques when using Affymetrix⃝R 3’ IVT microarrays in ecological microarray studies.

MiCA: web-based computational tools for the analysis of microbial community structure and composition based on T-RFLP of 16S rRNA genes

Proceedings. IEEE Computer Society Bioinformatics Conference, 2002

Analyses of microbial community structure based on terminal restriction fragment length polymorph... more Analyses of microbial community structure based on terminal restriction fragment length polymorphisms (T-RFLP) of 16S rRNA genes are hindered by the lack of computational tools needed to aid experimental design, and to archive and analyze large data sets. The aim of this research was to develop a suite of Web-based tools that would enable researchers to perform several tasks, including:

Quantum Genetic Algorithms

Recent developments in quantum technology have shown that quantum computers can provide a dramati... more Recent developments in quantum technology have shown that quantum computers can provide a dramatic advantage over classical computers for some algorithms. Since most problems of real interest for genetic algorithms (GAs) have a vast search space [Holland, 1975], it seems appropriate to consider how quantum parallelism can be applied to GAs. In this paper we present a simple quantum approach to genetic algorithms and analyze its benefits and drawbacks. This is significant because to date there are only a handful of quantum algorithms [Williams and Clearwater, 1997].

A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams

Big Data and Cognitive Computing

Outlier detection is a statistical procedure that aims to find suspicious events or items that ar... more Outlier detection is a statistical procedure that aims to find suspicious events or items that are different from the normal form of a dataset. It has drawn considerable interest in the field of data mining and machine learning. Outlier detection is important in many applications, including fraud detection in credit card transactions and network intrusion detection. There are two general types of outlier detection: global and local. Global outliers fall outside the normal range for an entire dataset, whereas local outliers may fall within the normal range for the entire dataset, but outside the normal range for the surrounding data points. This paper addresses local outlier detection. The best-known technique for local outlier detection is the Local Outlier Factor (LOF), a density-based technique. There are many LOF algorithms for a static data environment; however, these algorithms cannot be applied directly to data streams, which are an important type of big data. In general, loca...

Girls Build Excitement for Math from Scratch

Mathematics Teaching in the Middle School

To increase digital literacy and mathematical fluency, students electronically piece together det... more

Bee-Inspired Landmark Recognition in Robotic Navigation

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO '16 Companion, 2016

Research has shown that honeybees have a remarkable ability to use landmarks to travel miles to f... more Research has shown that honeybees have a remarkable ability to use landmarks to travel miles to find-and return to-a food source [1, 2]. The long-term goal of this research is to use physical robots, incorporating a variety of image processing approaches, to examine this ability. The results may also improve our ability to develop effective landmark guidance systems for robots. This research examines whether robots using a simple genetic algorithm (GA) and neural network (NN) that are trained to search for a target object adopt the use of landmarks to aid in the search-even when landmarks are not explicitly considered. This is a first step towards more complex experiments in less controlled environments. The simplified environment used in these experiments is controllable, making it easier to determine and understand the robot's behavior before moving to a more complex external environment. The robot uses a feed-forward, episodic NN for navigation-there is no inherent use of a memory bank of images. It is trained using Learning from Demonstration (LfD) to follow a simple search pattern independent of landmarks, but visual cues that could act as landmarks are present in the environment. Our results show that even under these conditions visual cues, i.e. landmarks, are incorporated in the learned search.

Stochastic training of a biologically plausible spino-neuromuscular system model

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, 2007

A primary goal of evolutionary robotics is to create systems that are as robust and adaptive as t... more A primary goal of evolutionary robotics is to create systems that are as robust and adaptive as the human body. Moving toward this goal often involves training control systems that process sensory information in a way similar to humans. Artificial neural networks have been an increasingly popular option for this because they consist of processing units that approximate the synaptic activity of biological signal processing units, i.e. neurons. In this paper we train a nonlinear recurrent spino-neuromuscular system (SNMS) model and compare the performance of genetic algorithms (GA)s, particle swarm optimizers (PSO)s, and GA/PSO hybrids. Several key features of the SNMS model have previously been modeled individually but have not been combined into a single model as is done here. The results show that each algorithm produces fit solutions and generates fundamental biological behaviors, such as tonic tension behaviors and triceps activation patterns, that are not explicitly trained.

Voting teams: a cooperative approach to non-typical problems using genetic programming

Gecco, 1999

... (There are 25 or 32 possible voting permutations. Only one of these has all members votingcor... more

Abstention Reduces Errors - decision Abstaining N-version Genetic Programming

Gecco, 2002

Limiting code growth in genetic programming

Resilient Individuals Improve Evolutionary Search

Alife, 2005

Results from the artificial life community show that under some conditions evolving populations c... more Results from the artificial life community show that under some conditions evolving populations converge on broader, but less fit peaks in the fitness landscape and avoid more fit, but narrower peaks. Results from the evolutionary computation community show that over time genotypes evolve to become more resilient, where resiliency (or genetic robustness) is defined as the ability of an individual to resist the potentially negative effects of genetic operations. This article demonstrates a previously unobserved evolutionary dynamic: in populations initially favoring a low, broad fitness peak, increases in resiliency result in the population shifting to a higher, narrower fitness peak. In these cases increasing resiliency is a necessary precondition for finding narrower peaks.

Redundant genes and the evolution of robustness

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, 2006

In this paper we demonstrate that pressure for robustness combined with function sets containing ... more

Genetic programming: parametric analysis of structure altering mutation techniques

Proceedings of the 2005 Workshops on Genetic and Evolutionary Computation, 2005

We hypothesize that the relationship between parameter settings, specically parameters controllin... more We hypothesize that the relationship between parameter settings, specically parameters controlling mutation, and performance is non-linear in genetic programs. Genetic programming environments have few means for a priori determination of appropriate parameters values. The hypothesized nonlinear behavior of genetic programming creates diculty in selecting parameter values for many problems. In this paper we study three structure altering mutation techniques using parametric analysis on a problem with scalable complexity. We nd through parameter analysis that two of the three mutation types tested exhibit nonlinear behavior. Higher mutation rates cause a larger degree of nonlinear behavior as measured by tness and computational eort. Characterization of the mutation techniques using parametric analysis conrms the nonlinear behavior. In addition, we propose an extension to the existing parameter setting taxonomy to include commonly used structure altering mutation attributes. Finally we show that the proportion of mutations applied to internal nodes, instead of leaf nodes, has a signicant eect on performance.

Genetic Programming Theory and Practice VI