Evolving neural controllers for terrestrial and extraterrestrial locomotion in an artificial quadruped

This study explores the use of a multi-objective evolutionary algorithm for the automatic synthesis of neural controllers for the quadrupedal locomotion of an artificial creature in a 3-dimensional, physics-based environment. The Paretofrontier Differential Evolution (PDE) algorithm is used to generate a Pareto optimal set of artificial neural networks that optimize the conflicting objectives of maximizing locomotion behavior and minimizing neural network complexity. The focus of this artificial life experiment is to firstly evolve embodied locomotion controllers for a physically simulated quadrupedal creature under terrestrial conditions (i.e. simulating Earth's gravity) and then to investigate the performance of the best evolved controller in this physically simulated creature under different extraterrestrial environments (i.e. simulating gravity on planets other than Earth). It was found that under all extraterrestrial conditions the artificial creature was still able to perform the required locomotion task while in the worst case, some minimal locomotion behavior was still achieved

Automated synthesis of mobile game environments and rulesets using a hybridized interactive evolutionary programming approach

By hybridizing Evolutionary Programming (EP) with Interactive Evolutionary Algorithm (IEA), game rules and its playing environment will be automatically generated for an arcade-type game that can be played on the Android mobile platform. In this study, mutation rates of 0.7 and 0.9 are used to generate both the game rules and the game environment for the mobile game. Players are used as the evaluator instead of the conventional mathematical fitness functions and hence the motivation for using high mutation rate is that they are able to generate higher levels of diversity during the optimization runs. This interactive mode of game-playing cum evaluation will enable the creation of games that can fit the user’s preferences as well as styles of game-playing. Experiments show a very positive result where very good evaluation scores were obtained from the users. This shows that with a high mutation rate, the hybridized EP with IEA approach can generate rules and environments that are well-accepted and liked by human players

Evolution of RF-signal cognition for wheeled mobile robots using pareto multi-objective optimization

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 controllers allowed the robots to home in towards the signal source with different movements’ behaviors. This study has thus revealed that the PDE-EMO algorithm can be practically used to automatically generate robust controllers for RFlocalization behavior in autonomous mobile robots

Exploration of mutation step sizes in the automated evolution of printable free-form 3D objects

3D printing is a comparatively new technology that is becoming ever more attractive to everyday practitioners and hobbyists due to its low start-up cost as well as making significant advancements in its printing process as well resolution and material variety. Implementation of EAs in the field of 3D printing is still in its infancy since 3D printing itself is a relatively new technology that has only become main stream due to its significant decrease in acquisition cost in the past 2-3 years. In this study, an EA in the form of Evolutionary Programming (EP) is used to automatically evolve 3D objects generated by Gielis’ Superformula. Objective: The focus of this study is to explore the mutation step size in hoping to create more diverse populations in the evolution of the generated 3D printable objects. In EP, the operator responsible for offspring generation is through the mutation process solely. Hence, the mutation step size has a direct and very significant impact on the diversity of the offspring generated. A fitness function was designed to evaluate the 3D objects and shapes generated by the Superformula. The parameters for the Superformula to generate 3D objects or shapes are m_1, m_2, n_(1,1), n_(1,2), n_(1,3), n_(2,1), n_(2,2), and n_(2,3). These parameters serve as a representation in EP and the mutation step size will affect the chances of these parameters’ values to change. To carry out this study, ten different mutation step sizes ranging from 0.1 to 1.0 in increments of 0.1 were used and run for five times. Results: The results indicate that the most aesthetically-pleasing as well as machine-printable results were obtained using the smallest mutation size of 0.1. Conclusion: Optimal setting for mutation rate can successful generate 3D-printable shapes that are aesthetically-pleasing using the proposed Gielis Superformula-based methodology

A time-critical investigation of parameter tuning in differential evolution for non-linear global optimization

Parameter searching is one of the most important aspects in getting favorable results in optimization problems. It is even more important if the optimization problems are limited by time constraints. In a limited time constraint problems, it is crucial for any algorithms to get the best results or near-optimum results. In a previous study, Differential Evolution (DE) has been found as one of the best performing algorithms under time constraints. As this has help in answering which algorithm that yields results that are near-optimum under a limited time constraint. Hence to further enhance the performance of DE under time constraint evaluation, a throughout parameter searching for population size, mutation constant and f constant have been carried out. CEC 2015 Global Optimization Competition’s 15 scalable test problems are used as test suite for this study. In the previous study the same test suits has been used and the results from DE will be use as the benchmark for this study since it shows the best results among the previous tested algorithms. Eight different populations size are used and they are 10, 30, 50, 100, 150, 200, 300, and 500. Each of these populations size will run with mutation constant of 0.1 until 0.9 and from 0.1 until 0.9. It was found that population size 100, Cr = 0.9, F=0.5 outperform the benchmark results. It is also observed from the results that good higher Cr around 0.8 and 0.9 with low F around 0.3 to 0.4 yields good results for DE under time constraints evaluatio

A comprehensive comparison of evolutionary optimization limited by number of evaluations against time constraints

In this study, the importance of optimization problems constrained by time is highlighted. Practically allevolutionary optimization studies have focused exclusively on the use of number of fitness evaluations as the constraining factor when comparing different evolutionary algorithms (EAs). This investigation represents the first study which empirically compares EAs based on time-based constraints against number of fitness evaluations. EAs which yield an optimum or near-optimum solutions is crucial for real-time optimization problems. Which EAs are able to provide near optimum solutions in time limited real-time optimization problems has never been answered before. To find out the answer for this question, four well-known and most commonly-used algorithms are tested. Particle swarm optimization (PSO), Differential Evolution (DE), Genetic Algorithms (GA), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are tested in three different setups of experiments. A comprehensive and latest global optimization benchmark test suite is used in the form of the CEC 2015 Global Optimization Competition’s 15 scalable test problems. The first experiment is to test the performance of these algorithms in expensive benchmark optimization problems that limit the number of fitness evaluations to 50N where N represents the number of optimization dimensions. The second experiment allows these algorithms to run up to the full 10000N evaluations. The last experiment will compare the performance of these algorithms limited by time to 300 milliseconds. The results obtained shows that DE can perform well in the 50N and 10000N evaluation. Critically, we have shown for the first time that in time-limited situations, DE is also the frontrunner by obtaining clearly better results compared to the other three well-known and widely used EAs

Evolutionary and population dynamics of 3 parents differential evolution (3PDE) using self-adaptive tuning methodologies

Differential Evolution is known for its simplicity and effectiveness as an evolutionary optimizer. In recent years, many researchers have focused on the exploration of Differential Evolution (DE). The objective of this paper is to show the evolutionary and population dynamics for the empirical testing on 3-Parents Differential Evolution (3PDE) for unconstrained function optimization (Teng et al. 2007). In this paper, 50 repeated evolutionary runs for each of 20 well-known benchmarks were carried out to test the proposed algorithms against the original 4-parents DE algorithm. As a result of the observed evolutionary dynamics, 3PDE-SAF performed the best among the preliminary proposed algorithms that included 3PDE-SACr and 3PDE-SACrF. Subsequently, 3PDE-SAF is chosen for the self-adaptive population size for testing dynamic population sizing methods using the absolute (3PDE-SAF-Abs) and relative (3PDE-SAF-Rel) population size encodings. The final result shows that 3PDE-SAF-Rel produced a better performance and convergence overall compared to all the other proposed algorithms, including the original DE. In terms of population dynamics, the population size in 3PDE-SAF-Abs exhibited disadvantageously high dynamics that caused less efficient results. On the other hand, the population size in 3PDE-SAF-Rel was observed to be approximately constant at ten times the number of variables being optimized, hence giving a better and more stable performance

Comparative analysis of electroencephalogram-based classification of user responses to statically vs. dynamically presented visual stimuli

Emotion is an important part of human and it plays important role in human communication. Nowadays, as the use of machine getting more common, the human computer interaction (HCI) has become important. The understanding of user could bring across a better aiding machine. The exploration of using EEG in understanding human is widely studied for benefit in several fields such as neuromarketing and HCI. In this study, we compare the use of 2 different stimuli (3D shapes with motion vs. 2D emotional images that are static) in attempting to classify positive versus negative feelings. A medical-grade 9-electrode Advance Brain Monitoring (ABM) B-alert X10 is used as the brain-computer interface (BCI) acquisition device to obtain the EEG signals. 4 subjects are involved in recording brain signals during viewing 2 types of stimuli. Feature extraction is then applied to the acquired EEG signals to obtain the alpha, beta, gamma, theta and delta rhythms as features using time frequency analysis. Support vector machine (SVM) and K-nearest neighbors (KNN) classifiers are used to train and classify positive and negative feelings for both stimuli using different channels and rhythms. The average accuracy of 3D motion shapes are better than the average accuracy of the 2D static emotional images for both SVM and KNN with 69.88% and 56.35% using SVM for 3D motion shapes and emotional images respectively, and also 65.31% and 55.45% using KNN for 3D motion shapes and emotional images respectively. This study shows that the parietal lobe are more informative in the classification of 3D motion shapes while the Fz channel of the frontal lobe is more informative in classification of 2D static emotional images