Replacement strategies in steady state genetic algorithms: Dynamic environments

Recent years have seen increasing numbers of applications of Evolutionary Algorithms to non-stationary environments such as on-line process control. Studies have indicated that Genetic Algorithms using "Steady State" models demonstrate a greater ability to track moving optima than those using "Generational" models, however implementing the former requires an additional choice of which members of the current population should be replaced by new offspring.In this paper a number of selection and replacement strategies are compared for use in Steady State Genetic Algorithms working as function optimisers in dynamic environments. In addition to an algorithm with fixed mutation rates, the strategies are also compared in algorithms employing Cobb\u27s Hypermutation method for tracking environmental changes. On-line and off-line metrics are used for comparison, which correspond to different types of real-world applications.In both cases it is shown that algorithms employing some kind of elitism outperform those that do not, which is related to previous studies on stationary environments. An investigation is made of various methods of implementing elitism, including an implicit method, "conservative" selection. It is shown that the latter, in addition to being computationally simpler, produces significantly better results on the problems used, and reasons are given for this behaviour

A self-organizing random immigrants genetic algorithm for dynamic optimization problems

This is the post-print version of the article. The official published version can be obtained from the link below - Copyright @ 2007 SpringerIn this paper a genetic algorithm is proposed where the worst individual and individuals with indices close to its index are replaced in every generation by randomly generated individuals for dynamic optimization problems. In the proposed genetic algorithm, the replacement of an individual can affect other individuals in a chain reaction. The new individuals are preserved in a subpopulation which is defined by the number of individuals created in the current chain reaction. If the values of fitness are similar, as is the case with small diversity, one single replacement can affect a large number of individuals in the population. This simple approach can take the system to a self-organizing behavior, which can be useful to control the diversity level of the population and hence allows the genetic algorithm to escape from local optima once the problem changes due to the dynamics.This work was supported by FAPESP (Proc. 04/04289-6)

Genetic algorithms with elitism-based immigrants for changing optimization problems

Copyright @ Springer-Verlag Berlin Heidelberg 2007.Addressing dynamic optimization problems has been a challenging task for the genetic algorithm community. Over the years, several approaches have been developed into genetic algorithms to enhance their performance in dynamic environments. One major approach is to maintain the diversity of the population, e.g., via random immigrants. This paper proposes an elitism-based immigrants scheme for genetic algorithms in dynamic environments. In the scheme, the elite from previous generation is used as the base to create immigrants via mutation to replace the worst individuals in the current population. This way, the introduced immigrants are more adapted to the changing environment. This paper also proposes a hybrid scheme that combines the elitism-based immigrants scheme with traditional random immigrants scheme to deal with significant changes. The experimental results show that the proposed elitism-based and hybrid immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments

Explicit memory schemes for evolutionary algorithms in dynamic environments

Copyright @ 2007 Springer-VerlagProblem 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

Genetic algorithms with self-organizing behaviour in dynamic environments

Copyright @ 2007 Springer-VerlagIn recent years, researchers from the genetic algorithm (GA) community have developed several approaches to enhance the performance of traditional GAs for dynamic optimization problems (DOPs). Among these approaches, one technique is to maintain the diversity of the population by inserting random immigrants into the population. This chapter investigates a self-organizing random immigrants scheme for GAs to address DOPs, where the worst individual and its next neighbours are replaced by random immigrants. In order to protect the newly introduced immigrants from being replaced by fitter individuals, they are placed in a subpopulation. In this way, individuals start to interact between themselves and, when the fitness of the individuals are close, one single replacement of an individual can affect a large number of individuals of the population in a chain reaction. The individuals in a subpopulation are not allowed to be replaced by individuals of the main population during the current chain reaction. The number of individuals in the subpopulation is given by the number of individuals created in the current chain reaction. It is important to observe that this simple approach can take the system to a self-organization behaviour, which can be useful for GAs in dynamic environments.Financial support was obtained from FAPESP (Proc. 04/04289-6)

Adaptive Balancing of a Bank of Sugar - Beet Presses Using a Genetic Algorithm with Variable Local Search Range

The paper describes an application of a genetic algorithm with a variable local search operator for load balancing in energy-intensive processing of sugar beet. The variable local search operator enables the genetic algorithm to track optima of the time-varying dynamic systems. It constitutes an adaptive tracking mechanism as it is triggered only when the running average of the best performing members of the population deteriorates. Results obtained on simulation suggest that the genetic algorithm-based optimiser can significantly improve energy efficiency of the real press station. The technique is believed to be a feasible engineering solution to a general load balancing problem between a number of parallel processing units with a measure of performance which reflects only an overall effectiveness of the system controlled and not that of individual processing elements. 1 Introduction In this paper we report on an application of a genetic algorithm (GA) with a variable local search o..

A Comparative Study of Steady State and Generational Genetic Algorithms for Use in Nonstationary Environments

. The objective of this study is a comparison of two models of a genetic algorithm - the generational and incremental/steady state genetic algorithms - for use in the nonstationary/dynamic environments. It is experimentally shown that selection of a suitable version of the genetic algorithm can improve performance of the genetic algorithm in such environments.This can extend ability of the genetic algorithm to track the environmental changes which are relatively small and occur with a low frequency without need to implement an additional technique for tracking changing optima. 1 Introduction The genetic algorithm is a proven search/optimisation technique [Holland 1975] based on an adaptive mechanism of the biological systems. In our previous work we showed that the genetic algorithm is a suitable on-line optimization method to balance the load of the presses in a sugar beet pressing station [Fogarty,Vavak,Cheng 1995] and to balance the fuel load in a multiple burner boiler [Vavak,Foga..

Performance of a Genetic Algorithm with Variable Local Search Range Relative to Frequency of the Environmental Changes

Although the genetic algorithm is a robust search technique, it is often unable to redirect its search to a different part of the search space, should the search landscape change. The variable local search operator was designed to enable the genetic algorithm to track optima of time-varying systems. It constitutes an adaptive tracking mechanism as it is triggered only when the running average of the best performing members of the population deteriorates. In this paper we compare the performance of the genetic algorithm which implements the variable local search operator with two alternative tracking mechanisms for periodically occurring environmental changes. The main advantage of the new tracking technique is that the VLS operator is able to provide sound results even for high frequencies of environmental change provided the degree of the change is relatively small as the search is initially confined to a restricted part of the fitness landscape. This is an important feature of the ne..

Learning the Local Search Range for Genetic Optimisation in Nonstationary Environments

In this paper we examine a modification to the genetic algorithm. The variable local search ("VLS") operator was designed to enable the genetic algorithm based on-line optimisers to track optima of time-varying dynamic systems. This feature is not to the detriment of its ability to provide sound results for the stationary environments. The operator matches the level of diversity introduced into the population with the "degree" of the environmental change by increasing population diversity only gradually. The paper also shows that performance of the designed tracking method can be further enhanced by integrating it with a simple exemplar-based incremental learning technique. It is believed that the designed technique will prove beneficial in the application of the genetic algorithm based approaches to industrial control problems. 1 Introduction Genetic algorithms (GAs) are proven optimisation and machine learning techniques based on an adaptive mechanism of biological systems [Holland ..