Genetic And Evolutionary Algorithms Research Articles

An analysis of the locality of binary representations in genetic and evolutionary algorithms

Mutation and recombination operators play a key role in determining the performance of Genetic and Evolutionary Algorithms (GEAs). Prior work has analyzed the effects of these operators on genotypic variation, often using locality metrics that measure the sensitivity and stability of genotype-phenotype representations to these operators. In this paper, we focus on an important subset of representations, namely nonredundant bitstring-to-integer representations, and analyze them through the lens of Rothlauf’s widely used locality metrics. Our main research question is, does strong locality predict good GEA performance for these representations? Our main findings, both theoretical and empirical, show the answer to be negative. To this end, we define locality metrics equivalent to Rothlauf’s that are tailored to our domain: thepoint localityfor single-bit mutation andgeneral localityfor recombination. With these definitions, we derive tight bounds and a closed-form expected value for point locality. For general locality we show that it is asymptotically equivalent across all representations and operators. We also reproduce three established GEA empirical results to understand the predictive power of point locality on GEA performance, focusing on two popular and often juxtaposed representations: standard binary and binary-reflected Gray.

A novel splicing/decomposable binary encoding and its operators for genetic and evolutionary algorithms

In this paper, we introduce a new genetic representation — a splicing/decomposable (S/D) binary encoding, which was proposed based on some theoretical guidance and existing recommendations for designing efficient genetic representations. The S/D binary representation can be spliced and decomposed to describe potential solutions of the problem with different precisions by different number of uniform-salient building blocks (BBs). According to the characteristics of the S/D binary representation, genetic and evolutionary algorithms (GEAs) can be applied from the high scaled to the low scaled BBs sequentially to avoid a noise from the competing BBs and improve GEAs’ performance. Our theoretical and empirical investigations reveal that the S/D binary representation is more proper than other existing binary encodings for GEAs searching. Moreover, we define a new genotypic distance on the S/D binary space, which is equivalent to the Euclidean distance on the real-valued space during GEAs convergence. Based on the new genotypic distance, GEAs can reliably and predictably solve problems of bounded complexity and the methods depended on the Euclidean distance for solving different kinds of optimization problems can be directly used on the S/D binary space.

A Lamarckian Approach for Neural Network Training

In Nature, living beings improve their adaptation to surrounding environments by means of two main orthogonal processes: evolution and lifetime learning. Within the Artificial Intelligence arena, both mechanisms inspired the development of non-orthodox problem solving tools, namely: Genetic and Evolutionary Algorithms (GEAs) and Artificial Neural Networks (ANNs). In the past, several gradient-based methods have been developed for ANN training, with considerable success. However, in some situations, these may lead to local minima in the error surface. Under this scenario, the combination of evolution and learning techniques may induce better results, desirably reaching global optima. Comparative tests that were carried out with classification and regression tasks, attest this claim.