A new memory based variable-length encoding genetic algorithm for multiobjective optimization

Authors:
Eduardo G. Carrano;Lívia A. Moreira;Ricardo H. C. Takahashi
Affiliations:
Centro Federal de Educação Tecnológica de Minas Gerais, Department of Computer Engineering, Belo Horizonte, MG, Brazil;Centro Federal de Educação Tecnológica de Minas Gerais, Department of Electrical Engineering, Belo Horizonte, MG, Brazil;Universidade Federal de Minas Gerais, Department of Mathematics, Belo Horizonte, MG, Brazil
Venue:
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Year:
2011

Citing 11
Cited 1

Fast hashing of variable-length text strings

Communications of the ACM
Tabu Search

Tabu Search
Introduction to Algorithms

Introduction to Algorithms
Evolution strategies –A comprehensive introduction

Natural Computing: an international journal
A Variant of Evolution Strategies for Vector Optimization

PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Rigorous analyses of simple diversity mechanisms

Proceedings of the 9th annual conference on Genetic and evolutionary computation
An overview of evolutionary algorithms in multiobjective optimization

Evolutionary Computation
An enhanced statistical approach for evolutionary algorithm comparison

Proceedings of the 10th annual conference on Genetic and evolutionary computation
A genetic algorithm that adaptively mutates and never revisits

IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms

IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Variable neighborhood multiobjective genetic algorithm for the optimization of routes on IP networks

EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper presents a new memory-based variable-length encoding genetic algorithm for solving multiobjective optimization problems. The proposed method is a binary implementation of the NSGA2 and it uses a Hash Table for storing all the solutions visited during algorithm evolution. This data structure makes possible to avoid the re-visitation of solutions and it provides recovering and storage of data with low computational cost. The algorithm memory is used for building crossover, mutation and local search operators with a parameterless variable-length encoding. These operators control the neighborhood based on the density of points already visited on the region of the new solution to be evaluated. Two classical multiobjective problems are used to compare two variations of the proposed algorithm and two variations of the binary NSGA2. A statistical analysis of the results indicates that the memory-based adaptive neighborhood operators are able to provide significant improvement of the quality of the Pareto-set approximations.