Making and breaking power laws in evolutionary algorithm population dynamics

Deepening our understanding of the characteristics and behaviors of population-based search algorithms remains an important ongoing challenge in Evolutionary Computation. To date however, most studies of Evolutionary Algorithms have only been able to take place within tightly restricted experimental conditions. For instance, many analytical methods can only be applied to canonical algorithmic forms or can only evaluate evolution over simple test functions. Analysis of EA behavior under more complex conditions is needed to broaden our understanding of this population-based search process. This paper presents an approach to analyzing EA behavior that can be applied to a diverse range of algorithm designs and environmental conditions. The approach is based on evaluating an individual’s impact on population dynamics using metrics derived from genealogical graphs.\ud From experiments conducted over a broad range of conditions, some important conclusions are drawn in this study. First, it is determined that very few individuals in an EA population have a significant influence on future population dynamics with the impact size fitting a power law distribution. The power law distribution indicates there is a non-negligible probability that single individuals will dominate the entire population, irrespective of population size. Two EA design features are however found to cause strong changes to this aspect of EA behavior: i) the population topology and ii) the introduction of completely new individuals. If the EA population topology has a long path length or if new (i.e. historically uncoupled) individuals are continually inserted into the population, then power law deviations are observed for large impact sizes. It is concluded that such EA designs can not be dominated by a small number of individuals and hence should theoretically be capable of exhibiting higher degrees of parallel search behavior

An Analysis of a Hybrid Evolutionary Algorithm by means of its Phylogenetic Information

The study conducted in this work analyses the interactions between different Evolutionary Algorithms when they are hybridized. For this purpose, the phylogenetic tree of the best solution reported by the hybrid algorithm is reconstructed, and the relationships among the ancestors of this solution are established. For each of these ancestors, the evolutionary techniques that generated that solution and the fitness increment introduced compared to its parents are recorded. The study reveals a structured interaction among the different evolutionary techniques that makes the hybrid algorithm to outperform each of its composing algorithms when executed individually. The Multiple Offspring Sampling framework has been used to develop the Hybrid EA studied in this work and the experiments have been conducted on the well-known CEC 2005 Benchmark for continuous optimizatio

Controladores obtenidos por neuroevolución

El aporte central de esta tesis radica en la definición de estrategias evolutivas que permiten obtener controladores neuronales aplicables directamente al área de la Robótica. A partir del método NEAT (NeuroEvolution of Augmenting Topologies) se ha definido una nueva estrategia con capacidad para combinar módulos neuronales entrenados previamente. El resultado de esta combinación permite obtener una arquitectura adecuada en menor tiempo. Como una segunda alternativa para reducir el tiempo de obtención del controlador se propone combinar las primeras etapas de evolución deNEATcon evolución por torneo binario. Finalmente, se plantea el uso de una minipoblación de controladores para lograr una adaptación a entornos dinámicos. Los resultados obtenidos fueron aplicados sobre un robot Kephera II con resultados satisfactorios.Facultad de Informátic

Controladores obtenidos por neuroevolución

El aporte central de esta tesis radica en la definición de estrategias evolutivas que permiten obtener controladores neuronales aplicables directamente al área de la Robótica. A partir del método NEAT (NeuroEvolution of Augmenting Topologies) se ha definido una nueva estrategia con capacidad para combinar módulos neuronales entrenados previamente. El resultado de esta combinación permite obtener una arquitectura adecuada en menor tiempo. Como una segunda alternativa para reducir el tiempo de obtención del controlador se propone combinar las primeras etapas de evolución deNEATcon evolución por torneo binario. Finalmente, se plantea el uso de una minipoblación de controladores para lograr una adaptación a entornos dinámicos. Los resultados obtenidos fueron aplicados sobre un robot Kephera II con resultados satisfactorios.Facultad de Informátic

Adaptation and self-organization in evolutionary algorithms

The objective of Evolutionary Computation is to solve practical problems (e.g.optimization, data mining) by simulating the mechanisms of natural evolution. This thesis addresses several topics related to adaptation and self-organization in evolving systems with the overall aims of improving the performance of Evolutionary Algorithms (EA), understanding its relation to natural evolution, and incorporating new mechanisms for mimicking complex biological systems. Part I of this thesis presents a new mechanism for allowing an EA to adapt its behavior in response to changes in the environment. Using the new approach, adaptation of EA behavior (i.e. control of EA design parameters) is driven by an analysis of population dynamics, as opposed to the more traditional use of fitness measurements. Comparisons with a number of adaptive control methods from the literature indicate substantial improvements in algorithm performance for a range of artificial and engineering design problems. Part II of this thesis involves a more thorough analysis of EA behavior based on the methods derived in Part 1. In particular, several properties of EA population dynamics are measured and compared with observations of evolutionary dynamics in nature. The results demonstrate that some large scale spatial and temporal features of EA dynamics are remarkably similar to their natural counterpart. Compatibility of EA with the Theory of Self-Organized Criticality is also discussed. Part III proposes fundamentally new directions in EA research which are inspired by the conclusions drawn in Part II. These changes involve new mechanisms which allow self-organization of the EA to occur in ways which extend beyond its common convergence in parameter space. In particular, network models for EA populations are developed where the network structure is dynamically coupled to EA population dynamics. Results indicate strong improvements in algorithm performance compared to cellular Genetic Algorithms and non-distributed EA designs. Furthermore, topological analysis indicates that the population network can spontaneously evolve to display similar characteristics to the interaction networks of complex biological systems