Self-adaptation of mutation distribution in evolutionary algorithms

This paper is posted here with permission from IEEE - Copyright @ 2007 IEEEThis paper proposes a self-adaptation method to control not only the mutation strength parameter, but also the mutation distribution for evolutionary algorithms. For this purpose, the isotropic g-Gaussian distribution is employed in the mutation operator. The g-Gaussian distribution allows to control the shape of the distribution by setting a real parameter g and can reproduce either finite second moment distributions or infinite second moment distributions. In the proposed method, the real parameter q of the g-Gaussian distribution is encoded in the chromosome of an individual and is allowed to evolve. An evolutionary programming algorithm with the proposed idea is presented. Experiments were carried out to study the performance of the proposed algorithm

From parameter control to search control: Parameter Control Abstraction in Evolutionary Algorithms

This paper presents a method to encapsulate parameters of evolutionary algorithms and to create an abstraction that simplifies the control and the understanding of the internal behavior of the algorithm. A fuzzy model is used to learn the effects of parameters over the search process. Then, high-level strategies can be defined to modify parameters automatically in order to achieve a scheduledlevel of balance between exploration and exploitation during the search. We experimented supervised control strategies and autonomous schemes that adjust parameters dynamically. Experiments have been performed on the Quadratic Assignment Problem in order to analyze the strengths and weaknesses of each approach. Possible improvements of the general methodology are also discussed

States based evolutionary algorithm

Choosing the suitable representation, the operators and the values of the parameters of an evolutionary algorithm is one of the main problems to design an efficient algorithm for one particular optimization problem. This additional information to the evolutionary algorithm generally is called the algorithm parameter, or parameter. This work introduces a new evolutionary algorithm, States based Evolutionary Algorithm which is able to combine different evolutionary algorithms with different parameters included different representations in order to control the parameters and to take the advantage of each possible evolution algorithm during the optimization process. This paper gives first experimental arguments of the efficiency of the States based EA

Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem

Improvements in evolutionary algorithms (EAs) consider multirecombination, allowing multiple crossover operations on a pair of parents (MCPC, multiple crossovers per couple) or on a set of multiple parents (MCMP, multiple crossovers on multiple parents). Evolutionary algorithms have been successfully applied to solve scheduling problems. MCMP-STUD and MCMP-SRI are novel MCMP variants, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents In this paper the proposal is to generate the stud-breeding individual by means of a robust conventional heuristic, the CDS. In a multirecombined EA, setting of parameters n1 (number of crossovers) and n2 (number of parents) remained as an open question. In previous works; they were empirically determined, or a deterministic rule was applied. In this paper self adaptation of parameters n1 and n2 is implemented, the idea is to code the parameters within the chromosome and undergo genetic operations. Hence it is expected that better parameter values be more intensively propagated. The present paper discusses different multi-recombined methods and contrasts their performance when different parameter control methods are applied, to find the minimum makespan for selected instances of the FSSP.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem

Improvements in evolutionary algorithms (EAs) consider multirecombination, allowing multiple crossover operations on a pair of parents (MCPC, multiple crossovers per couple) or on a set of multiple parents (MCMP, multiple crossovers on multiple parents). Evolutionary algorithms have been successfully applied to solve scheduling problems. MCMP-STUD and MCMP-SRI are novel MCMP variants, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents In this paper the proposal is to generate the stud-breeding individual by means of a robust conventional heuristic, the CDS. In a multirecombined EA, setting of parameters n1 (number of crossovers) and n2 (number of parents) remained as an open question. In previous works; they were empirically determined, or a deterministic rule was applied. In this paper self adaptation of parameters n1 and n2 is implemented, the idea is to code the parameters within the chromosome and undergo genetic operations. Hence it is expected that better parameter values be more intensively propagated. The present paper discusses different multi-recombined methods and contrasts their performance when different parameter control methods are applied, to find the minimum makespan for selected instances of the FSSP.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Self-adaptation in non-elitist evolutionary algorithms on discrete problems with unknown structure

A key challenge to make effective use of evolutionary algorithms is to choose appropriate settings for their parameters. However, the appropriate parameter setting generally depends on the structure of the optimisation problem, which is often unknown to the user. Non-deterministic parameter control mechanisms adjust parameters using information obtained from the evolutionary process. Self-adaptation -- where parameter settings are encoded in the chromosomes of individuals and evolve through mutation and crossover -- is a popular parameter control mechanism in evolutionary strategies. However, there is little theoretical evidence that self-adaptation is effective, and self-adaptation has largely been ignored by the discrete evolutionary computation community. Here we show through a theoretical runtime analysis that a non-elitist, discrete evolutionary algorithm which self-adapts its mutation rate not only outperforms EAs which use static mutation rates on \leadingones, but also improves asymptotically on an EA using a state-of-the-art control mechanism. The structure of this problem depends on a parameter $k$, which is \emph{a priori} unknown to the algorithm, and which is needed to appropriately set a fixed mutation rate. The self-adaptive EA achieves the same asymptotic runtime as if this parameter was known to the algorithm beforehand, which is an asymptotic speedup for this problem compared to all other EAs previously studied. An experimental study of how the mutation-rates evolve show that they respond adequately to a diverse range of problem structures. These results suggest that self-adaptation should be adopted more broadly as a parameter control mechanism in discrete, non-elitist evolutionary algorithms.Comment: To appear in IEEE Transactions of Evolutionary Computatio