Studying Parallel Evolutionary Algorithms: The cellular Programming Case

Parallel evolutionary algorithms, studied to some extent over the past few years, have proven empirically worthwhile—though there seems to be lacking a better understanding of their workings. In this paper we concentrate on cellular (fine-grained) models, presenting a number of statistical measures, both at the genotypic and phenotypic levels. We demonstrate the application and utility of these measures on a specific example, that of the cellular programming evolutionary algorithm, when used to evolve solutions to a hard problem in the cellular-automata domain, known as synchronization

A computational model of evolution: haploidy versus diploidy

In this paper, the study of diploidy is introduced like and important mechanism for memory reinforcement in artificial environments where adaptation is very important. The individuals of this ecosystem are able to genetically "learn" the best behaviour for survival. Critical changes, happening in the environmental conditions, require the presence of diploidy to ensure the survival of species. By means of new gene-dominance configurations, a way to shield the individuals from erroneous selection is provided. These two concepts appear like important elements for artificial systems which have to evolve in environments with some degree of instability.Publicad

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

Optimal phenotypic plasticity in a stochastic environment minimizes the cost/benefit ratio

This paper addresses the question of optimal phenotypic plasticity as a response to environmental fluctuations while optimizing the cost/benefit ratio, where the cost is energetic expense of plasticity, and benefit is fitness. The dispersion matrix \Sigma of the genes' response (H = ln|\Sigma|) is used: (i) in a numerical model as a metric of the phenotypic variance reduction in the course of fitness optimization, then (ii) in an analytical model, in order to optimize parameters under the constraint of limited energy availability. Results lead to speculate that such optimized organisms should maximize their exergy and thus the direct/indirect work they exert on the habitat. It is shown that the optimal cost/benefit ratio belongs to an interval in which differences between individuals should not substantially modify their fitness. Consequently, even in the case of an ideal population, close to the optimal plasticity, a certain level of genetic diversity should be long conserved, and a part, still to be determined, of intra-populations genetic diversity probably stem from environment fluctuations. Species confronted to monotonous factors should be less plastic than vicariant species experiencing heterogeneous environments. Analogies with the MaxEnt algorithm of E.T. Jaynes (1957) are discussed, leading to the conjecture that this method may be applied even in case of multivariate but non multinormal distributions of the responses

International Symposium on Evolutionary Breeding in Cereals

Evolutionary plant breeding has a long history, but has so far not become part of mainstream breeding research, nor has it been implemented in practice to any substantial degree. However, over the last decade, research in evolutionary plant breeding has markedly intensified. For example, there are currently major research projects on-going in this area, including the EU funded project SOLIBAM, the Wheat Breeding LINK project in the UK, and the Danish Biobreed project. Also, a new 3-year international research project called COBRA on this topic is due to start in March 2013. Funded by the CORE Organic 2 Eranet the project brings together over 40 partner organizations from 18 European countries. In addition, interest in evolutionary plant breeding is growing among farmers, breeders and policy makers. In fact, there are currently encouraging developments in the imminent revision of seed legislation in Europe that could lead to more room for evolutionary plant breeding approaches in the future. This renewed interest in evolutionary plant breeding is partly due to the recognition that mainstream plant breeding is limited in terms of its engagement with end users, i.e. farmers and growers. More urgently however, effects of climate change on agricultural production have become more noticeable and there is also a growing awareness of increasing resource constraints; together, these will create more stressful growing conditions for agricultural crops. With this background, it is now being recognized that crops need to be able to cope with more variable, contrasting, fluctuating, and generally more unpredictable growing conditions. To be able to deal with this large and increasing environmental variability, plant breeding needs to become more decentralized and diversified. Evolutionary plant breeding offers great potential in this respect. The contributions collated from this symposium explore this potential as well as the limitations of evolutionary plant breeding. While they only show a part of the on-going research activities in Europe, we hope that these proceedings provide inspiration both for further research and for implementation in practice

Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum.

The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits

Comparing and Combining Lexicase Selection and Novelty Search

Lexicase selection and novelty search, two parent selection methods used in evolutionary computation, emphasize exploring widely in the search space more than traditional methods such as tournament selection. However, lexicase selection is not explicitly driven to select for novelty in the population, and novelty search suffers from lack of direction toward a goal, especially in unconstrained, highly-dimensional spaces. We combine the strengths of lexicase selection and novelty search by creating a novelty score for each test case, and adding those novelty scores to the normal error values used in lexicase selection. We use this new novelty-lexicase selection to solve automatic program synthesis problems, and find it significantly outperforms both novelty search and lexicase selection. Additionally, we find that novelty search has very little success in the problem domain of program synthesis. We explore the effects of each of these methods on population diversity and long-term problem solving performance, and give evidence to support the hypothesis that novelty-lexicase selection resists converging to local optima better than lexicase selection