Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems

Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios

The influence of mutation on population dynamics in multiobjective genetic programming

Using multiobjective genetic programming with a complexity objective to overcome tree bloat is usually very successful but can sometimes lead to undesirable collapse of the population to all single-node trees. In this paper we report a detailed examination of why and when collapse occurs. We have used different types of crossover and mutation operators (depth-fair and sub-tree), different evolutionary approaches (generational and steady-state), and different datasets (6-parity Boolean and a range of benchmark machine learning problems) to strengthen our conclusion. We conclude that mutation has a vital role in preventing population collapse by counterbalancing parsimony pressure and preserving population diversity. Also, mutation controls the size of the generated individuals which tends to dominate the time needed for fitness evaluation and therefore the whole evolutionary process. Further, the average size of the individuals in a GP population depends on the evolutionary approach employed. We also demonstrate that mutation has a wider role than merely culling single-node individuals from the population; even within a diversity-preserving algorithm such as SPEA2 mutation has a role in preserving diversity

A Survey on Software Testing Techniques using Genetic Algorithm

The overall aim of the software industry is to ensure delivery of high quality software to the end user. To ensure high quality software, it is required to test software. Testing ensures that software meets user specifications and requirements. However, the field of software testing has a number of underlying issues like effective generation of test cases, prioritisation of test cases etc which need to be tackled. These issues demand on effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of evolutionary algorithms for automatic test generation has been an area of interest for many researchers. Genetic Algorithm (GA) is one such form of evolutionary algorithms. In this research paper, we present a survey of GA approach for addressing the various issues encountered during software testing.Comment: 13 Page

Cultural Learning in a Dynamic Environment: an Analysis of Both Fitness and Diversity in Populations of Neural Network Agents

Evolutionary learning is a learning model that can be described as the iterative Darwinian process of fitness-based selection and genetic transfer of information leading to populations of higher fitness. Cultural learning describes the process of information transfer between individuals in a population through non-genetic means. Cultural learning has been simulated by combining genetic algorithms and neural networks using a teacher/pupil scenario where highly fit individuals are selected as teachers and instruct the next generation. This paper examines the effects of cultural learning on the evolutionary process of a population of neural networks. In particular, the paper examines the genotypic and phenotypic diversity of a population as well as its fitness. Using these measurements, it is possible to examine the effects of cultural learning on the population's genetic makeup. Furthermore, the paper examines whether cultural learning provides a more robust learning mechanism in the face of environmental changes. Three benchmark tasks have been chosen as the evolutionary task for the population: the bit-parity problem, the game of tic-tac-toe and the game of connect-four. Experiments are conducted with populations employing evolutionary learning alone and populations combining evolutionary and cultural learning in an environment that changes dramatically.Cultural Learning, Dynamic Environments, Diversity, Multi-Agent Systems, Artificial Life

Improving resiliency using graph based evolutionary algorithms

Resiliency is an important characteristic of any system. It signifies the ability of a system to survive and recover from unprecedented disruptions. Various characteristics exist that indicate the level of resiliency in a system. One of these attributes is the adaptability of the system. This adaptability can be enhanced by redundancy present within the system. In the context of system design, redundancy can be achieved by having a diverse set of good designs for that particular system. Evolutionary algorithms are widely used in creating designs for engineering systems, as they perform well on discontinuous and/or high dimensional problems. One method to control the diversity of solutions within an evolutionary algorithm is the use of combinatorial graphs, or graph based evolutionary algorithms. This diversity of solutions is key factor to enhance the redundancy of a system design. In this work, the way how graph based evolutionary algorithms generate diverse solutions is investigated by examining the influence of representation and mutation. This allows for greater understanding of the exploratory nature of each representation and how they can control the number of solution generated within a trial. The results of this research are then applied to the Travelling [sic] Salesman Problem, a known NP hard problem often used as a surrogate for logistic or network design problems. When the redundancy in system design is improved, adaptability can be achieved by placing an agent to initiate a transfer to other good solutions in the event of a disruption in network connectivity, making it possible to improve the resiliency of the system --Abstract, page iii

Quantitative Perspectives on Fifty Years of the Journal of the History of Biology

Journal of the History of Biology provides a fifty-year long record for examining the evolution of the history of biology as a scholarly discipline. In this paper, we present a new dataset and preliminary quantitative analysis of the thematic content of JHB from the perspectives of geography, organisms, and thematic fields. The geographic diversity of authors whose work appears in JHB has increased steadily since 1968, but the geographic coverage of the content of JHB articles remains strongly lopsided toward the United States, United Kingdom, and western Europe and has diversified much less dramatically over time. The taxonomic diversity of organisms discussed in JHB increased steadily between 1968 and the late 1990s but declined in later years, mirroring broader patterns of diversification previously reported in the biomedical research literature. Finally, we used a combination of topic modeling and nonlinear dimensionality reduction techniques to develop a model of multi-article fields within JHB. We found evidence for directional changes in the representation of fields on multiple scales. The diversity of JHB with regard to the representation of thematic fields has increased overall, with most of that diversification occurring in recent years. Drawing on the dataset generated in the course of this analysis, as well as web services in the emerging digital history and philosophy of science ecosystem, we have developed an interactive web platform for exploring the content of JHB, and we provide a brief overview of the platform in this article. As a whole, the data and analyses presented here provide a starting-place for further critical reflection on the evolution of the history of biology over the past half-century.Comment: 45 pages, 14 figures, 4 table