Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments

Abstract

Summary. Problem optimization in dynamic environments has atrracted a growing interest from the evolutionary computation community in reccent years due to its importance in real world optimization problems. Several approaches have been developed to enhance the performance of evolutionary algorithms for dynamic optimization problems, of which the memory scheme is a major one. This chapter investigates the application of explicit memory schemes for evolutionary algorithms in dynamic environments. Two kinds of explicit memory schemes: direct memory and associative memory, are studied within two classes of evolutionary algorithms: genetic algorithms and univariate marginal distribution algorithms for dynamic optimization problems. Based on a series of systematically constructed dynamic test environments, experiments are carried out to investigate these explicit memory schemes and the performance of direct and associative memory schemes are campared and analysed. The experimental results show the efficiency of the memory schemes for evolutionary algorithms in dynamic environments, especially when the environment changes cyclically. The experimental results also indicate that the effect of the memory schemes depends not only on the dynamic problems and dynamic environments but also on the evolutionary algorithm used.

Optimization in dynamic environments: a survey on problems, methods and measures

“…In this sense, interesting benchmarks are obtained when the ''peak'' function from (Eriksson and Olsson 2002;Esquivel andCoello Coello 2004, 2006;Morrison 2004;Peng and Reynolds 2004;Saleem and Reynolds 2000) Dynamic Ackley function (Schönemann 2004(Schönemann , 2007 Dynamic bit-matching (Jin and Branke 2005;Mori and Kita 2000a, b;Stanhope and Daida 1999) Dynamic deceptive functions (Tinós and Yang 2007b;Wang et al 2009a, b;Yang 2003Yang , 2006bYang , 2007Yang and Tinós 2007a, b;Yao 2005, 2008) Dynamic knapsack problem (Branke et al 2006a, b;Jin and Branke 2005;Karaman et al 2005;Rohlfshagen and Yao 2009a, b;Simões and Costa 2003;Wang et al 2009b;Yang 2008;Yang and Tinós 2007a, b;Yang and Yao 2005) Dynamic Onemax function (Droste 2003;Fernandes et al 2008;Wang et al 2009a;Yang 2003Yang , 2005Yang , 2007Yang , 2008Yang and Tinós 2007a, b;Yang and Yao 2008) Dynamic plateau functions (Wang et al 2009a;Yang 2006bYang , 2008 Dynamic problem generator (Jin and Sendhoff 2004;Li and Yang 2008a;Morrison and De Jong 1999;…”

Multipopulation-Based Differential Evolution with Speciation-Based Response to Dynamic Environments

“…Since late 1980s, it started to attract several researchers and results a huge increase in the number of publications in this area. Comprehensive surveys on the adaptation and application of EAs for tackling DOPs can be found in [2,7,8]. A number of articles to solve DOPs using Genetic Algorithm (GA) have published by the researchers [8][9][10].…”

Evolutionary programming with ensemble of explicit memories for dynamic optimization

“…For example, after a change is detected, the mutation rate can be raised to increase the diversity of the population [16], or else, niching methods can be used to spread out the population so that it may adapt to changes more easily [17]. Memory-based approaches [18], [19] have also been proposed to recall information from past generations, which is especially useful when the new optimum is not far from previous locations. In addition, we may apply multi-population techniques to track multiple peaks in the fitness landscape [20], [21].…”