Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference - GECCO '12, 2012
Several techniques have been developed for allowing genetic programming systems to produce progra... more Several techniques have been developed for allowing genetic programming systems to produce programs that make use of subroutines, macros, and other modular program structures. A recently proposed technique, based on the "tagging" and tag-based retrieval of blocks of code, has been shown to have novel and desirable features, but this was demonstrated only within the context of the PushGP genetic programming system. Following a suggestion in the GECCO-2011 publication on this technique we show here how tag-based modules can be incorporated into a more standard tree-based genetic programming system. We describe the technique in detail along with some possible extensions, outline arguments for its simplicity and potential power, and present results obtained using the technique on problems for which other modularization techniques have been shown to be useful. The results are mixed; substantial benefits are seen on the lawnmower problem but not on the Boolean even-4-parity problem. We discuss the observed results and directions for future research.
The number of offspring produced by each parent-that is, the fecundity of reproducing individuals... more The number of offspring produced by each parent-that is, the fecundity of reproducing individuals-varies among evolutionary computation methods and settings. In most prior work fecundity has been tied directly to selectivity, with higher selection pressure giving rise to higher fecundity among individuals selected to reproduce. In nature, however, there is a wider variety of strategies, with different organisms producing different numbers of offspring under the influence of a range of factors including not only selection pressure but also other factors such as environmental stability and competition within a niche. In this work we consider possible lessons that may be drawn from nature's approaches to these issues and applied to evolutionary computation systems. In particular, we consider ways in which fecundity can be dissociated from selectivity and situations in which it may be beneficial to do so. We present a simple modification to the standard evolutionary algorithm, called decimation, that permits high fecundity in conjunction with modest selection pressure and which could be used in various forms of evolutionary computation. We also present a simple example, showing that decimation can improve the problem-solving performance of a genetic algorithm when applied to a deceptive problem.
Proceedings of the 2020 Genetic and Evolutionary Computation Conference
In recent years the field of genetic programming has made significant advances towards automatic ... more In recent years the field of genetic programming has made significant advances towards automatic programming. Research and development of contemporary program synthesis methods, such as PushGP and Grammar Guided Genetic Programming, can produce programs that solve problems typically assigned in introductory academic settings. These problems focus on a narrow, predetermined set of simple data structures, basic control flow patterns, and primitive, non-overlapping data types (without, for example, inheritance or composite types). Few, if any, genetic programming methods for program synthesis have convincingly demonstrated the capability of synthesizing programs that use arbitrary data types, data structures, and specifications that are drawn from existing codebases. In this paper, we introduce Code Building Genetic Programming (CBGP) as a framework within which this can be done, by leveraging programming language features such as reflection and first-class specifications. CBGP produces a computational graph that can be executed or translated into source code of a host language. To demonstrate the novel capabilities of CBGP, we present results on new benchmarks that use non-primitive, polymorphic data types as well as some standard program synthesis benchmarks.
Lexicase parent selection filters the population by considering one random training case at a tim... more Lexicase parent selection filters the population by considering one random training case at a time, eliminating any individuals with errors for the current case that are worse than the best error in the selection pool, until a single individual remains. This process often stops before considering all training cases, meaning that it will ignore the error values on any cases that were not yet considered. Lexicase selection can therefore select specialist individuals that have poor errors on some training cases, if they have great errors on others and those errors come near the start of the random list of cases used for the parent selection event in question. We hypothesize here that selecting these specialists, which may have poor total error, plays an important role in lexicase selection's observed performance advantages over error-aggregating parent selection methods such as tournament selection, which select specialists much less frequently. We conduct experiments examining thi...
Lexicase selection is a parent selection method that considers training cases individually, rathe... more Lexicase selection is a parent selection method that considers training cases individually, rather than in aggregate, when performing parent selection. Whereas previous work has demonstrated the ability of lexicase selection to solve difficult problems in program synthesis and symbolic regression, the central goal of this paper is to develop the theoretical underpinnings that explain its performance. To this end, we derive an analytical formula that gives the expected probabilities of selection under lexicase selection, given a population and its behavior. In addition, we expand upon the relation of lexicase selection to many-objective optimization methods to describe the behavior of lexicase selection, which is to select individuals on the boundaries of Pareto fronts in high-dimensional space. We show analytically why lexicase selection performs more poorly for certain sizes of population and training cases, and show why it has been shown to perform more poorly in continuous error ...
We explore tradeo#s between classical communication and entanglement-generating powers of unitary... more We explore tradeo#s between classical communication and entanglement-generating powers of unitary 2-qubit gates. The exploration is aided by a computational search technique called genetic programming.
Proceedings of the Genetic and Evolutionary Computation Conference Companion
is is an extended abstract for an invited keynote presentation at the 7th Workshop on Evolutionar... more is is an extended abstract for an invited keynote presentation at the 7th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA). We rst outline the motivation, primary mechanisms, and prior results of the evolutionary computation technique called "autoconstructive evolution." We then brie y describe a collection of recent enhancements to the technique, along with a few preliminary results of ongoing experimental work. CCS CONCEPTS •Computing methodologies → Genetic programming; Articial life; •So ware and its engineering → Genetic programming;
Proceedings of the Genetic and Evolutionary Computation Conference Companion
In the Push programming language, all sequences of valid symbols with balanced parentheses are sy... more In the Push programming language, all sequences of valid symbols with balanced parentheses are syntactically valid and execute without error. Push nonetheless supports arbitrary data types and is Turing complete. This combination of features makes it useful as the target language for program induction in genetic programming systems, for which it was developed, and potentially also in other program induction systems. Prior Push implementations have generally been designed for use only within the host language in which they were implemented, often in conjunction with a specific program induction system that is written in the same host language. In this paper we present Plushi, a modular, embeddable Push interpreter that is designed to interoperate with program induction systems written in any language. CCS CONCEPTS • Software and its engineering → Automatic programming; Genetic programming;
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Prior work has demonstrated that genetic programming systems often maintain higher levels of popu... more Prior work has demonstrated that genetic programming systems often maintain higher levels of population diversity when using lexicase selection than when using other parent selection methods, and that the use of lexicase selection improves problem-solving performance in many circumstances. It has been suggested that it is not only the maintenance of diversity that is responsible for the performance of lexicase selection, but more specifically the production and maintenance of "specialists" that matters, where specialists are defined to be individuals with the lowest error, relative to the rest of the population, on a small number of training cases regardless of total error. Here we provide results of experiments that uphold this suggestion by tracking the numbers of specialists selected by lexicase selection and by tournament selection in a genetic programming system solving software synthesis problems. Our results also show that lexicase selection selects parents with poor total error far more frequently than tournament selection, even near the ends of successful runs, suggesting that such selections are integral to the improved problem-solving performance of lexicase selection. CCS CONCEPTS • Software and its engineering → Automatic programming; Genetic programming;
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Previous work has demonstrated the utility of graph databases as a tool for collecting and analyz... more Previous work has demonstrated the utility of graph databases as a tool for collecting and analyzing ancestry in evolutionary computation runs. at work focused on sections of individual runs, whereas this poster illustrates the application of these ideas on the entirety of large runs (up to one million individuals) and combinations of multiple runs. Here we use these tools to generate graphs showing all the ancestors of successful individuals from a variety of stack-based genetic programming runs on so ware synthesis problems. ese graphs highlight important moments in the evolutionary process. ey also allow us to compare the dynamics when using di erent evolutionary tools, such as di erent selection mechanisms or representations, as well as comparing the dynamics for successful and unsuccessful runs.
Cooperation in evolving populations of agents has been explained as arising from kin selection, r... more Cooperation in evolving populations of agents has been explained as arising from kin selection, reciprocity during repeated interactions, and indirect reciprocity through agent reputations. All of these mechanisms require signican t agent capabilities, but recent research using computational models has shown that arbitrary markers called \tags" can be used to achieve signican t levels of cooperation even in the absence of memory, repeated interactions or knowledge of kin. This is important because it helps to explain the evolution of cooperation in organisms with limited cognitive capabilities, and also because it may help us to engineer cooperative behaviors in multi-agent systems. The computational models used in previous studies, however, have typically been constrained such that cooperation is the only viable strategy for gaining an evolutionary advantage. Here we show that tagmediated recognition can lead to signican t levels of cooperation in a less constrained articial l...
This paper discusses the role of culture in the evolution of cognitive systems. We define “cultur... more This paper discusses the role of culture in the evolution of cognitive systems. We define “culture” as any information transmitted between individuals and between generations by nongenetic means. Experiments are presented that use genetic programming systems that include special mechanisms for cultural transmission of information. These systems evolve computer programs that perform cognitive tasks including mathematical function mapping and action selection in a virtual world. The data show that the presence of culture-supporting mechanisms can have a clear beneficial impact on the evolvability of correct programs. The implications that these results may have for cognitive science are briefly discussed.
We report how breve, a simulation environment with rich 3d graphics, was used to discover signifi... more We report how breve, a simulation environment with rich 3d graphics, was used to discover significant patterns in the dynamics of a system that evolves controllers for swarms of goal-directed agents. These patterns were discovered via visualization in the sense that we had not considered their relevance or thought to look for them initially, but they became obvious upon visually observing the behavior of the system. In this paper we briefly describe breve and the system of evolving swarms that we implemented within it. We then describe two discovered properties of the evolutionary dynamics of the system: transitions to/from genetic drift regimes and the emergence of collective or multicellular organization. We comment more generally on the utility of 3d visualization for the discovery of biologically significant phenomena and briefly describe our ongoing work in this area. Pointers are provided to on-line resources including source code and animations that demonstrate several of the...
This paper discusses the evolution of diversifying reproduction. We measured the average differen... more This paper discusses the evolution of diversifying reproduction. We measured the average difference between mothers and their children, the number of species, and the degree of adaptation in evolving populations of endogenously diversifying digital organisms using the Pushpop system. The data show that the number of species in adaptive populations is higher than in nonadaptive populations, while the variance in the differences between mothers and their children is less for adaptive populations than for non-adaptive populations. In other words, in adaptive populations the species were more numerous and the diversification processes were more reliable.
Genetic programming can be used to automatically discover algorithms for quantum computers that a... more Genetic programming can be used to automatically discover algorithms for quantum computers that are more efficient than any classical computer algorithms for the same problems. In this paper we exhibit the first evolved betterthan-classical quantum algorithm, for Deutsch’s “early promise” problem. We also demonstrate a technique for evolving scalable quantum gate arrays and discuss other issues in the application of genetic programming to quantum computation and vice versa. 1. Quantum Computing Quantum computers are computational devices that use atomic-scale objects, for example 2-state particles, to store and manipulate information (Steane, 1997; for an elementary on-line tutorial see Braunstein, 1995; for an introduction for the general reader see Milburn, 1997). The physics of these devices allows them to do things that common digital (henceforth “classical”) computers cannot. Although quantum computers and classical computers appear to be bound by the same limits of Turing comp...
The growth of program size during evolution (code "bloat") is a well-documented and wel... more The growth of program size during evolution (code "bloat") is a well-documented and well-studied problem in genetic programming. This paper examines the use of "size fair" genetic operators to combat code bloat in the PushGP genetic programming system. Size fair operators are compared to naive operators and to operators that use "node selection" as described by Koza. The effects of the operator choices are assessed in runs on symbolic regression, parity and multiplexor problems (2,700 runs in total). The results show that the size fair operators control bloat well while producing unusually parsimonious solutions. The computational effort required to find a solution using size fair operators is about equal to, or slightly better than, the effort required using the comparison operators.
This paper shows how ontogenetic programming , an enhancement to the genetic programming methodol... more This paper shows how ontogenetic programming , an enhancement to the genetic programming methodology, a l l o ws for the automatic generation of adaptive programs. Programs produced by o n togenetic programming may include calls to self-modiication operators. By permitting runtime program self-modiication, these operators allow e v olved programs to further adapt to their environments. In this paper the onto-genetic programming methodology is described and two examples of its use are presented, one for binary sequence prediction and the other for action selection in a virtual world. In both cases the inclusion of self-modiication operators has a clear positive impact on the ability of genetic programming to produce successful programs.
Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference - GECCO '12, 2012
Several techniques have been developed for allowing genetic programming systems to produce progra... more Several techniques have been developed for allowing genetic programming systems to produce programs that make use of subroutines, macros, and other modular program structures. A recently proposed technique, based on the "tagging" and tag-based retrieval of blocks of code, has been shown to have novel and desirable features, but this was demonstrated only within the context of the PushGP genetic programming system. Following a suggestion in the GECCO-2011 publication on this technique we show here how tag-based modules can be incorporated into a more standard tree-based genetic programming system. We describe the technique in detail along with some possible extensions, outline arguments for its simplicity and potential power, and present results obtained using the technique on problems for which other modularization techniques have been shown to be useful. The results are mixed; substantial benefits are seen on the lawnmower problem but not on the Boolean even-4-parity problem. We discuss the observed results and directions for future research.
The number of offspring produced by each parent-that is, the fecundity of reproducing individuals... more The number of offspring produced by each parent-that is, the fecundity of reproducing individuals-varies among evolutionary computation methods and settings. In most prior work fecundity has been tied directly to selectivity, with higher selection pressure giving rise to higher fecundity among individuals selected to reproduce. In nature, however, there is a wider variety of strategies, with different organisms producing different numbers of offspring under the influence of a range of factors including not only selection pressure but also other factors such as environmental stability and competition within a niche. In this work we consider possible lessons that may be drawn from nature's approaches to these issues and applied to evolutionary computation systems. In particular, we consider ways in which fecundity can be dissociated from selectivity and situations in which it may be beneficial to do so. We present a simple modification to the standard evolutionary algorithm, called decimation, that permits high fecundity in conjunction with modest selection pressure and which could be used in various forms of evolutionary computation. We also present a simple example, showing that decimation can improve the problem-solving performance of a genetic algorithm when applied to a deceptive problem.
Proceedings of the 2020 Genetic and Evolutionary Computation Conference
In recent years the field of genetic programming has made significant advances towards automatic ... more In recent years the field of genetic programming has made significant advances towards automatic programming. Research and development of contemporary program synthesis methods, such as PushGP and Grammar Guided Genetic Programming, can produce programs that solve problems typically assigned in introductory academic settings. These problems focus on a narrow, predetermined set of simple data structures, basic control flow patterns, and primitive, non-overlapping data types (without, for example, inheritance or composite types). Few, if any, genetic programming methods for program synthesis have convincingly demonstrated the capability of synthesizing programs that use arbitrary data types, data structures, and specifications that are drawn from existing codebases. In this paper, we introduce Code Building Genetic Programming (CBGP) as a framework within which this can be done, by leveraging programming language features such as reflection and first-class specifications. CBGP produces a computational graph that can be executed or translated into source code of a host language. To demonstrate the novel capabilities of CBGP, we present results on new benchmarks that use non-primitive, polymorphic data types as well as some standard program synthesis benchmarks.
Lexicase parent selection filters the population by considering one random training case at a tim... more Lexicase parent selection filters the population by considering one random training case at a time, eliminating any individuals with errors for the current case that are worse than the best error in the selection pool, until a single individual remains. This process often stops before considering all training cases, meaning that it will ignore the error values on any cases that were not yet considered. Lexicase selection can therefore select specialist individuals that have poor errors on some training cases, if they have great errors on others and those errors come near the start of the random list of cases used for the parent selection event in question. We hypothesize here that selecting these specialists, which may have poor total error, plays an important role in lexicase selection's observed performance advantages over error-aggregating parent selection methods such as tournament selection, which select specialists much less frequently. We conduct experiments examining thi...
Lexicase selection is a parent selection method that considers training cases individually, rathe... more Lexicase selection is a parent selection method that considers training cases individually, rather than in aggregate, when performing parent selection. Whereas previous work has demonstrated the ability of lexicase selection to solve difficult problems in program synthesis and symbolic regression, the central goal of this paper is to develop the theoretical underpinnings that explain its performance. To this end, we derive an analytical formula that gives the expected probabilities of selection under lexicase selection, given a population and its behavior. In addition, we expand upon the relation of lexicase selection to many-objective optimization methods to describe the behavior of lexicase selection, which is to select individuals on the boundaries of Pareto fronts in high-dimensional space. We show analytically why lexicase selection performs more poorly for certain sizes of population and training cases, and show why it has been shown to perform more poorly in continuous error ...
We explore tradeo#s between classical communication and entanglement-generating powers of unitary... more We explore tradeo#s between classical communication and entanglement-generating powers of unitary 2-qubit gates. The exploration is aided by a computational search technique called genetic programming.
Proceedings of the Genetic and Evolutionary Computation Conference Companion
is is an extended abstract for an invited keynote presentation at the 7th Workshop on Evolutionar... more is is an extended abstract for an invited keynote presentation at the 7th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA). We rst outline the motivation, primary mechanisms, and prior results of the evolutionary computation technique called "autoconstructive evolution." We then brie y describe a collection of recent enhancements to the technique, along with a few preliminary results of ongoing experimental work. CCS CONCEPTS •Computing methodologies → Genetic programming; Articial life; •So ware and its engineering → Genetic programming;
Proceedings of the Genetic and Evolutionary Computation Conference Companion
In the Push programming language, all sequences of valid symbols with balanced parentheses are sy... more In the Push programming language, all sequences of valid symbols with balanced parentheses are syntactically valid and execute without error. Push nonetheless supports arbitrary data types and is Turing complete. This combination of features makes it useful as the target language for program induction in genetic programming systems, for which it was developed, and potentially also in other program induction systems. Prior Push implementations have generally been designed for use only within the host language in which they were implemented, often in conjunction with a specific program induction system that is written in the same host language. In this paper we present Plushi, a modular, embeddable Push interpreter that is designed to interoperate with program induction systems written in any language. CCS CONCEPTS • Software and its engineering → Automatic programming; Genetic programming;
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Prior work has demonstrated that genetic programming systems often maintain higher levels of popu... more Prior work has demonstrated that genetic programming systems often maintain higher levels of population diversity when using lexicase selection than when using other parent selection methods, and that the use of lexicase selection improves problem-solving performance in many circumstances. It has been suggested that it is not only the maintenance of diversity that is responsible for the performance of lexicase selection, but more specifically the production and maintenance of "specialists" that matters, where specialists are defined to be individuals with the lowest error, relative to the rest of the population, on a small number of training cases regardless of total error. Here we provide results of experiments that uphold this suggestion by tracking the numbers of specialists selected by lexicase selection and by tournament selection in a genetic programming system solving software synthesis problems. Our results also show that lexicase selection selects parents with poor total error far more frequently than tournament selection, even near the ends of successful runs, suggesting that such selections are integral to the improved problem-solving performance of lexicase selection. CCS CONCEPTS • Software and its engineering → Automatic programming; Genetic programming;
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Previous work has demonstrated the utility of graph databases as a tool for collecting and analyz... more Previous work has demonstrated the utility of graph databases as a tool for collecting and analyzing ancestry in evolutionary computation runs. at work focused on sections of individual runs, whereas this poster illustrates the application of these ideas on the entirety of large runs (up to one million individuals) and combinations of multiple runs. Here we use these tools to generate graphs showing all the ancestors of successful individuals from a variety of stack-based genetic programming runs on so ware synthesis problems. ese graphs highlight important moments in the evolutionary process. ey also allow us to compare the dynamics when using di erent evolutionary tools, such as di erent selection mechanisms or representations, as well as comparing the dynamics for successful and unsuccessful runs.
Cooperation in evolving populations of agents has been explained as arising from kin selection, r... more Cooperation in evolving populations of agents has been explained as arising from kin selection, reciprocity during repeated interactions, and indirect reciprocity through agent reputations. All of these mechanisms require signican t agent capabilities, but recent research using computational models has shown that arbitrary markers called \tags" can be used to achieve signican t levels of cooperation even in the absence of memory, repeated interactions or knowledge of kin. This is important because it helps to explain the evolution of cooperation in organisms with limited cognitive capabilities, and also because it may help us to engineer cooperative behaviors in multi-agent systems. The computational models used in previous studies, however, have typically been constrained such that cooperation is the only viable strategy for gaining an evolutionary advantage. Here we show that tagmediated recognition can lead to signican t levels of cooperation in a less constrained articial l...
This paper discusses the role of culture in the evolution of cognitive systems. We define “cultur... more This paper discusses the role of culture in the evolution of cognitive systems. We define “culture” as any information transmitted between individuals and between generations by nongenetic means. Experiments are presented that use genetic programming systems that include special mechanisms for cultural transmission of information. These systems evolve computer programs that perform cognitive tasks including mathematical function mapping and action selection in a virtual world. The data show that the presence of culture-supporting mechanisms can have a clear beneficial impact on the evolvability of correct programs. The implications that these results may have for cognitive science are briefly discussed.
We report how breve, a simulation environment with rich 3d graphics, was used to discover signifi... more We report how breve, a simulation environment with rich 3d graphics, was used to discover significant patterns in the dynamics of a system that evolves controllers for swarms of goal-directed agents. These patterns were discovered via visualization in the sense that we had not considered their relevance or thought to look for them initially, but they became obvious upon visually observing the behavior of the system. In this paper we briefly describe breve and the system of evolving swarms that we implemented within it. We then describe two discovered properties of the evolutionary dynamics of the system: transitions to/from genetic drift regimes and the emergence of collective or multicellular organization. We comment more generally on the utility of 3d visualization for the discovery of biologically significant phenomena and briefly describe our ongoing work in this area. Pointers are provided to on-line resources including source code and animations that demonstrate several of the...
This paper discusses the evolution of diversifying reproduction. We measured the average differen... more This paper discusses the evolution of diversifying reproduction. We measured the average difference between mothers and their children, the number of species, and the degree of adaptation in evolving populations of endogenously diversifying digital organisms using the Pushpop system. The data show that the number of species in adaptive populations is higher than in nonadaptive populations, while the variance in the differences between mothers and their children is less for adaptive populations than for non-adaptive populations. In other words, in adaptive populations the species were more numerous and the diversification processes were more reliable.
Genetic programming can be used to automatically discover algorithms for quantum computers that a... more Genetic programming can be used to automatically discover algorithms for quantum computers that are more efficient than any classical computer algorithms for the same problems. In this paper we exhibit the first evolved betterthan-classical quantum algorithm, for Deutsch’s “early promise” problem. We also demonstrate a technique for evolving scalable quantum gate arrays and discuss other issues in the application of genetic programming to quantum computation and vice versa. 1. Quantum Computing Quantum computers are computational devices that use atomic-scale objects, for example 2-state particles, to store and manipulate information (Steane, 1997; for an elementary on-line tutorial see Braunstein, 1995; for an introduction for the general reader see Milburn, 1997). The physics of these devices allows them to do things that common digital (henceforth “classical”) computers cannot. Although quantum computers and classical computers appear to be bound by the same limits of Turing comp...
The growth of program size during evolution (code "bloat") is a well-documented and wel... more The growth of program size during evolution (code "bloat") is a well-documented and well-studied problem in genetic programming. This paper examines the use of "size fair" genetic operators to combat code bloat in the PushGP genetic programming system. Size fair operators are compared to naive operators and to operators that use "node selection" as described by Koza. The effects of the operator choices are assessed in runs on symbolic regression, parity and multiplexor problems (2,700 runs in total). The results show that the size fair operators control bloat well while producing unusually parsimonious solutions. The computational effort required to find a solution using size fair operators is about equal to, or slightly better than, the effort required using the comparison operators.
This paper shows how ontogenetic programming , an enhancement to the genetic programming methodol... more This paper shows how ontogenetic programming , an enhancement to the genetic programming methodology, a l l o ws for the automatic generation of adaptive programs. Programs produced by o n togenetic programming may include calls to self-modiication operators. By permitting runtime program self-modiication, these operators allow e v olved programs to further adapt to their environments. In this paper the onto-genetic programming methodology is described and two examples of its use are presented, one for binary sequence prediction and the other for action selection in a virtual world. In both cases the inclusion of self-modiication operators has a clear positive impact on the ability of genetic programming to produce successful programs.
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