A comparative study of adaptive mutation operators for metaheuristics

Genetic algorithms (GAs) are a class of stochastic optimization methods inspired by the principles of natural evolution. Adaptation of strategy parameters and genetic operators has become an important and promising research area in GAs. Many researchers are applying adaptive techniques to guide the search of GAs toward optimum solutions. Mutation is a key component of GAs. It is a variation operator to create diversity for GAs. This paper investigates several adaptive mutation operators, including population level adaptive mutation operators and gene level adaptive mutation operators, for GAs and compares their performance based on a set of uni-modal and multi-modal benchmark problems. The experimental results show that the gene level adaptive mutation operators are usually more efficient than the population level adaptive mutation operators for GAs

Use of Statistical Outlier Detection Method in Adaptive\ud Evolutionary Algorithms

In this paper, the issue of adapting probabilities for Evolutionary Algorithm (EA) search operators is revisited. A framework is devised for distinguishing between measurements of performance and the interpretation of those measurements for purposes of adaptation. Several examples of measurements and statistical interpretations are provided. Probability value adaptation is tested using an EA with 10 search operators against 10 test problems with results indicating that both the type of measurement and its statistical interpretation play significant roles in EA performance. We also find that selecting operators based on the prevalence of outliers rather than on average performance is able to provide considerable improvements to\ud adaptive methods and soundly outperforms the non-adaptive\ud case

Use of statistical outlier detection method in adaptive evolutionary algorithms

In this paper, the issue of adapting probabilities for Evolutionary Algorithm (EA) search operators is revisited. A framework is devised for distinguishing between measurements of performance and the interpretation of those measurements for purposes of adaptation. Several examples of measurements and statistical interpretations are provided. Probability value adaptation is tested using an EA with 10 search operators against 10 test problems with results indicating that both the type of measurement and its statistical interpretation play significant roles in EA performance. We also find that selecting operators based on the prevalence of outliers rather than on average performance is able to provide considerable improvements to adaptive methods and soundly outperforms the non-adaptive case

A MOS-based Dynamic Memetic Differential Evolution Algorithm for Continuous Optimization: A Scalability Test

Continuous optimization is one of the areas with more activity in the field of heuristic optimization. Many algorithms have been proposed and compared on several benchmarks of functions, with different performance depending on the problems. For this reason, the combination of different search strategies seems desirable to obtain the best performance of each of these approaches. This contribution explores the use of a hybrid memetic algorithm based on the multiple offspring framework. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods that separately obtain very competitive results. This algorithm has been tested with the benchmark problems and conditions defined for the special issue of the Soft Computing Journal on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. The proposed algorithm obtained the best results compared with both its composing algorithms and a set of reference algorithms that were proposed for the special issue

Impact analysis of crossovers in a multi-objective evolutionary algorithm

Multi-objective optimization has become mainstream because several real-world problems are naturally posed as a Multi-objective optimization problems (MOPs) in all fields of engineering and science. Usually MOPs consist of more than two conflicting objective functions and that demand trade-off solutions. Multi-objective evolutionary algorithms (MOEAs) are extremely useful and well-suited for solving MOPs due to population based nature. MOEAs evolve its population of solutions in a natural way and searched for compromise solutions in single simulation run unlike traditional methods. These algorithms make use of various intrinsic search operators in efficient manners. In this paper, we experimentally study the impact of different multiple crossovers in multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework and evaluate its performance over test instances of 2009 IEEE congress on evolutionary computation (CEC?09) developed for MOEAs competition. Based on our carried out experiment, we observe that used variation operators are considered to main source to improve the algorithmic performance of MOEA/D for dealing with CEC?09 complicated test problems

The Road to VEGAS: Guiding the Search over Neutral Networks

VEGAS (Varying Evolvability-Guided Adaptive Search) is a new methodology proposed to deal with the neutrality property of some optimization problems. ts main feature is to consider the whole neutral network rather than an arbitrary solution. Moreover, VEGAS is designed to escape from plateaus based on the evolvability of solution and a multi-armed bandit. Experiments are conducted on NK-landscapes with neutrality. Results show the importance of considering the whole neutral network and of guiding the search cleverly. The impact of the level of neutrality and of the exploration-exploitation trade-off are deeply analyzed.Comment: Genetic And Evolutionary Computation Conference, Dublin : Ireland (2011

Probability Matching-based Adaptive Strategy Selection vs. Uniform Strategy Selection within Differential Evolution: An Empirical Comparison on the BBOB-2010 Noiseless Testbed

International audienceDifferent strategies can be used for the generation of new candidate solutions on the Differential Evolution algorithm. However, the definition of which of them should be applied to the problem at hand is not trivial, besides being a sensitive choice with relation to the algorithm performance. In this paper, we use the BBOB-2010 noiseless benchmarking suite to further empirically validate the Probability Matching-based Adaptive Strategy Selection (PMAdapSS-DE), a method proposed to automatically select the mutation strategy to be applied, based on the relative fitness improvements recently achieved by the application of each of the available strategies on the current optimization process. It is compared with what would be a timeless (naive) choice, the uniform strategy selection within the same sub-set of strategies

Non-standard approaches to evolutionary algorithms in an optimization dilemma

Este artículo pretende ser un ensayo de tipo crítico-reflexivo, que toma como base el conocido problema que se presenta en el Dilema Exploración-Explotación (DEE) cuando se trabaja con Algoritmos Evolutivos (AEs) y se centra en las propuestas, en este aspecto, tanto de los enfoques tradicionales, como de los enfoques recientes que manejan la población de soluciones (individuos) de manera distinta a los AEs estándar, a saber: el Modelo Evolutivo Aprendible (MEVA) y los Algoritmos de Estimación de Distribuciones (AEDs).This article is intended to be a critical-reflexive essay based on a well-known problem: the Exploration-Exploitation Dilemma (DEE, in spanish) when working with Evolutionary Algorithms (AEs, in Spanish) and, in this respect, focuses on the proposals that study both: traditional approaches and recent ones handling the population of solutions (individuals) differently from the AEs standard, which are: the Learnable Evolution Model (MEVA, in spanish) and the Estimation of Distribution Algorithms (AEDs, in spanish