Recognition and identification of aesthetic preference is indispensable in industrial design. Hum... more Recognition and identification of aesthetic preference is indispensable in industrial design. Humans tend to pursue products with aesthetic values and make buying decisions based on their aesthetic preferences. The existence of neuromarketing is to understand consumer responses toward marketing stimuli by using imaging techniques and recognition of physiological parameters. Numerous studies have been done to understand the relationship between human, art and aesthetics. In this paper, we present a novel preference-based measurement of user aesthetics using electroencephalogram (EEG) signals for virtual 3D shapes with motion. The 3D shapes are designed to appear like bracelets, which is generated by using the Gielis superformula. EEG signals were collected by using a medical grade device, the B-Alert X10 from advance brain monitoring, with a sampling frequency of 256 Hz and resolution of 16 bits. The signals obtained when viewing 3D bracelet shapes were decomposed into alpha, beta, theta, gamma and delta rhythm by using time-frequency analysis, then classified into two classes, namely like and dislike by using support vector machines and K-nearest neighbors (KNN) classifiers respectively. Classification accuracy of up to 80 % was obtained by using KNN with the alpha, theta and delta rhythms as the features extracted from frontal channels, Fz, F3 and F4 to classify two classes, like and dislike.
Proceedings of the 5th Wseas Iasme International Conference on Systems Theory and Scientific Computation, 2005
Although the Differential Evolution (DE) algorithm has been shown to be a simple yet powerful evo... more Although the Differential Evolution (DE) algorithm has been shown to be a simple yet powerful evolutionary algorithm for optimizing continuous functions, users are still faced with the problem of preliminary testing and hand-tuning of the evolutionary parameters prior to commencing the actual optimization process. As a solution, self-adaptation has been found to be highly beneficial in automatically and dynamically adjusting evolutionary parameters such as crossover rates and mutation rates. In this paper, we present a first attempt at selfadapting the population size parameter in addition to self-adapting crossover and mutation rates. Firstly, our main objective is to demonstrate the feasibility of self-adapting the population size parameter in DE. Using the F1-F5 benchmark test problems proposed by De Jong, we showed that DE with self-adaptive populations produced highly competitive results compared to a conventional DE algorithm with static populations. In addition to reducing the number of parameters used in DE, the proposed algorithm actually outperformed the conventional DE algorithm for one of the test problems. It was also found that that an absolute encoding methodology for self-adapting population size in DE produced results with greater optimization reliability compared to a relative encoding methodology.
ABSTRACT The Generalized Generation Gap (G3) algorithm is one of the most efficient and effective... more ABSTRACT The Generalized Generation Gap (G3) algorithm is one of the most efficient and effective state-of-the-art realcoded genetic algorithms (RCGAs) for unconstrained global optimization. However, its performance on multimodal optimization problems is known to ...
Abstract The study of evolving physically situated and virtually embodied artificial creatures ha... more Abstract The study of evolving physically situated and virtually embodied artificial creatures has been a hotbed of research in recent years. The availability and maturation of commercial-off-the-shelf physics engines coupled with the dramatic increase of personal ...
This paper proposes a novel perspective to the use of evolutionary multiobjective optimization (E... more This paper proposes a novel perspective to the use of evolutionary multiobjective optimization (EMO) as a paradigm for the characterization of complexity. Our objective is not to introduce a new measure of complexity but rather providing a framework for comparing the complexity of an object across different complexity scales. EMO provides a practical (from artificial life practitioners' perspective) yet mathematically-sound methodology that, by combining existing measures of complexity, enables complexity comparisons to be conducted. We show empirically that the partial order feature inherited in the Pareto concept exhibits characteristics which are suitable for comparing between the complexities of artificially evolved embodied organisms. Moreover, we present a first attempt at quantifying the morphological complexity of organisms as well as their behaviors.
This paper reviews the use of artificial evolution as a means for automatic generation of locomot... more This paper reviews the use of artificial evolution as a means for automatic generation of locomotion controllers for physical robots and autonomous agents from the perspectives of evolutionary objectives and control architecture. An overview beginning with the pioneering works in ...
This paper explores a new approach of using a multi-objective evolutionary algorithm (MOEA) to ev... more This paper explores a new approach of using a multi-objective evolutionary algorithm (MOEA) to evolve robot controllers in performing phototaxis task while avoiding obstacles or navigating through a maze in a simulated environment, to overcome problems involving ...
A major drawback associated with the use of artificial neural networks for data mining is their l... more A major drawback associated with the use of artificial neural networks for data mining is their lack of explanation capability. While they can achieve a high predictive accuracy rate, the knowledge captured is not transparent and cannot be verified by domain experts. In this paper, Artificial Neural Network Tree (ANNT), i.e. ANN training preceded by Decision Tree rules extraction method is presented to overcome the comprehensibility problem of ANN. Two pruning techniques are used with the ANNT algorithm; one is to prune the neural network and another to prune the tree. Both of these pruning methods are evaluated to see the effect on ANNT in terms of accuracy, comprehensibility and fidelity. generalize and learn from data imitates the human's ability to learn from experience and often outperform the other models in terms of predictive accuracy. However, most of these applications primarily focus at developing networks with high predictive accuracy without paying any attention to explain how the classifications are being made or the knowledge that is learned. In ANN, the knowledge learned is represented at a subsymbolic level in terms of connections and weights. It is like a black box providing little insight into how decisions are made. They have no explicit, declarative knowledge structure that allows the representation and generation of explanation structures. Thus, knowledge captured by ANN is not transparent to users and cannot be verified by domain experts.
One of the most efficient Real-Coded Genetic Algorithms (RCGAs) for function optimization current... more One of the most efficient Real-Coded Genetic Algorithms (RCGAs) for function optimization currently is the G3-PCX (Parent-Centric Crossover) algorithm. However, its performance for solving complex multimodal problems with highly deceptive fitness landscapes is known to be poor ...
This study investigates the augmentation of the crossover-only G3-PCX evolutionary optimization a... more This study investigates the augmentation of the crossover-only G3-PCX evolutionary optimization algorithm with mutational diversity for solving multimodal global optimization problems. The objective is to empirically test and compare the optimization precision and dynamics of the two ...
Abstract: This paper investigates the utilization of a multiobjective approach for evolving artif... more Abstract: This paper investigates the utilization of a multiobjective approach for evolving artificial neural networks (ANNs) that act as controllers for a collective box-pushing task based on radio frequency (RF)-localization of a group of virtual E-puck robots simulated in ...
This article describes a simulation model in which a multi-objective approach is utilized for evo... more This article describes a simulation model in which a multi-objective approach is utilized for evolving an artificial neural networks (ANNs) controller for an autonomous mobile robot. A mobile robot is simulated in a 3D, physics-based environment for the RF-localization behavior. The elitist Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal set of ANNs that could optimize two objectives in a single run; (1) maximize the mobile robot homing behavior whilst (2) minimize the hidden neurons involved in the feed-forward ANN. The generated controllers are evaluated on its performances based on Pareto analysis. Furthermore, the generated controllers are tested with four different environments particularly for robustness assessment. The testing environments are different from the environment in which evolution was conducted. Interestingly however, the testing results showed some of the mobile robots are still robust to the testing environments. The...
Recognition and identification of aesthetic preference is indispensable in industrial design. Hum... more Recognition and identification of aesthetic preference is indispensable in industrial design. Humans tend to pursue products with aesthetic values and make buying decisions based on their aesthetic preferences. The existence of neuromarketing is to understand consumer responses toward marketing stimuli by using imaging techniques and recognition of physiological parameters. Numerous studies have been done to understand the relationship between human, art and aesthetics. In this paper, we present a novel preference-based measurement of user aesthetics using electroencephalogram (EEG) signals for virtual 3D shapes with motion. The 3D shapes are designed to appear like bracelets, which is generated by using the Gielis superformula. EEG signals were collected by using a medical grade device, the B-Alert X10 from advance brain monitoring, with a sampling frequency of 256 Hz and resolution of 16 bits. The signals obtained when viewing 3D bracelet shapes were decomposed into alpha, beta, theta, gamma and delta rhythm by using time-frequency analysis, then classified into two classes, namely like and dislike by using support vector machines and K-nearest neighbors (KNN) classifiers respectively. Classification accuracy of up to 80 % was obtained by using KNN with the alpha, theta and delta rhythms as the features extracted from frontal channels, Fz, F3 and F4 to classify two classes, like and dislike.
Proceedings of the 5th Wseas Iasme International Conference on Systems Theory and Scientific Computation, 2005
Although the Differential Evolution (DE) algorithm has been shown to be a simple yet powerful evo... more Although the Differential Evolution (DE) algorithm has been shown to be a simple yet powerful evolutionary algorithm for optimizing continuous functions, users are still faced with the problem of preliminary testing and hand-tuning of the evolutionary parameters prior to commencing the actual optimization process. As a solution, self-adaptation has been found to be highly beneficial in automatically and dynamically adjusting evolutionary parameters such as crossover rates and mutation rates. In this paper, we present a first attempt at selfadapting the population size parameter in addition to self-adapting crossover and mutation rates. Firstly, our main objective is to demonstrate the feasibility of self-adapting the population size parameter in DE. Using the F1-F5 benchmark test problems proposed by De Jong, we showed that DE with self-adaptive populations produced highly competitive results compared to a conventional DE algorithm with static populations. In addition to reducing the number of parameters used in DE, the proposed algorithm actually outperformed the conventional DE algorithm for one of the test problems. It was also found that that an absolute encoding methodology for self-adapting population size in DE produced results with greater optimization reliability compared to a relative encoding methodology.
ABSTRACT The Generalized Generation Gap (G3) algorithm is one of the most efficient and effective... more ABSTRACT The Generalized Generation Gap (G3) algorithm is one of the most efficient and effective state-of-the-art realcoded genetic algorithms (RCGAs) for unconstrained global optimization. However, its performance on multimodal optimization problems is known to ...
Abstract The study of evolving physically situated and virtually embodied artificial creatures ha... more Abstract The study of evolving physically situated and virtually embodied artificial creatures has been a hotbed of research in recent years. The availability and maturation of commercial-off-the-shelf physics engines coupled with the dramatic increase of personal ...
This paper proposes a novel perspective to the use of evolutionary multiobjective optimization (E... more This paper proposes a novel perspective to the use of evolutionary multiobjective optimization (EMO) as a paradigm for the characterization of complexity. Our objective is not to introduce a new measure of complexity but rather providing a framework for comparing the complexity of an object across different complexity scales. EMO provides a practical (from artificial life practitioners' perspective) yet mathematically-sound methodology that, by combining existing measures of complexity, enables complexity comparisons to be conducted. We show empirically that the partial order feature inherited in the Pareto concept exhibits characteristics which are suitable for comparing between the complexities of artificially evolved embodied organisms. Moreover, we present a first attempt at quantifying the morphological complexity of organisms as well as their behaviors.
This paper reviews the use of artificial evolution as a means for automatic generation of locomot... more This paper reviews the use of artificial evolution as a means for automatic generation of locomotion controllers for physical robots and autonomous agents from the perspectives of evolutionary objectives and control architecture. An overview beginning with the pioneering works in ...
This paper explores a new approach of using a multi-objective evolutionary algorithm (MOEA) to ev... more This paper explores a new approach of using a multi-objective evolutionary algorithm (MOEA) to evolve robot controllers in performing phototaxis task while avoiding obstacles or navigating through a maze in a simulated environment, to overcome problems involving ...
A major drawback associated with the use of artificial neural networks for data mining is their l... more A major drawback associated with the use of artificial neural networks for data mining is their lack of explanation capability. While they can achieve a high predictive accuracy rate, the knowledge captured is not transparent and cannot be verified by domain experts. In this paper, Artificial Neural Network Tree (ANNT), i.e. ANN training preceded by Decision Tree rules extraction method is presented to overcome the comprehensibility problem of ANN. Two pruning techniques are used with the ANNT algorithm; one is to prune the neural network and another to prune the tree. Both of these pruning methods are evaluated to see the effect on ANNT in terms of accuracy, comprehensibility and fidelity. generalize and learn from data imitates the human's ability to learn from experience and often outperform the other models in terms of predictive accuracy. However, most of these applications primarily focus at developing networks with high predictive accuracy without paying any attention to explain how the classifications are being made or the knowledge that is learned. In ANN, the knowledge learned is represented at a subsymbolic level in terms of connections and weights. It is like a black box providing little insight into how decisions are made. They have no explicit, declarative knowledge structure that allows the representation and generation of explanation structures. Thus, knowledge captured by ANN is not transparent to users and cannot be verified by domain experts.
One of the most efficient Real-Coded Genetic Algorithms (RCGAs) for function optimization current... more One of the most efficient Real-Coded Genetic Algorithms (RCGAs) for function optimization currently is the G3-PCX (Parent-Centric Crossover) algorithm. However, its performance for solving complex multimodal problems with highly deceptive fitness landscapes is known to be poor ...
This study investigates the augmentation of the crossover-only G3-PCX evolutionary optimization a... more This study investigates the augmentation of the crossover-only G3-PCX evolutionary optimization algorithm with mutational diversity for solving multimodal global optimization problems. The objective is to empirically test and compare the optimization precision and dynamics of the two ...
Abstract: This paper investigates the utilization of a multiobjective approach for evolving artif... more Abstract: This paper investigates the utilization of a multiobjective approach for evolving artificial neural networks (ANNs) that act as controllers for a collective box-pushing task based on radio frequency (RF)-localization of a group of virtual E-puck robots simulated in ...
This article describes a simulation model in which a multi-objective approach is utilized for evo... more This article describes a simulation model in which a multi-objective approach is utilized for evolving an artificial neural networks (ANNs) controller for an autonomous mobile robot. A mobile robot is simulated in a 3D, physics-based environment for the RF-localization behavior. The elitist Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal set of ANNs that could optimize two objectives in a single run; (1) maximize the mobile robot homing behavior whilst (2) minimize the hidden neurons involved in the feed-forward ANN. The generated controllers are evaluated on its performances based on Pareto analysis. Furthermore, the generated controllers are tested with four different environments particularly for robustness assessment. The testing environments are different from the environment in which evolution was conducted. Interestingly however, the testing results showed some of the mobile robots are still robust to the testing environments. The...
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