Searching under multi-evolutionary pressures

Authors:
Hussein A. Abbass;Kalyanmoy Deb
Affiliations:
Artificial Life and Adaptive Robotics Lab, School of Computer Science, University of New South Wales, Australian Defence Force Academy Campus, Canberra, Australia;Mechanical Engineering Department, Indian Institute of Technology, Kanpur, Kanpur, India
Venue:
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Year:
2003

Citing 7
Cited 12

Serial and Parallel Genetic Algorithms as Function Optimizers

Proceedings of the 5th International Conference on Genetic Algorithms
A Memetic Pareto Evolutionary Approach to Artificial Neural Networks

AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
An analysis of the behavior of a class of genetic adaptive systems.

An analysis of the behavior of a class of genetic adaptive systems.
Information Characteristics and the Structure of Landscapes

Evolutionary Computation
Predictive models for the breeder genetic algorithm i. continuous parameter optimization

Evolutionary Computation
The science of breeding and its application to the breeder genetic algorithm (bga)

Evolutionary Computation
An evolutionary artificial neural networks approach for breast cancer diagnosis

Artificial Intelligence in Medicine

Speeding up backpropagation using multiobjective evolutionary algorithms

Neural Computation
Automatic generation of controllers for embodied legged organisms: a Pareto evolutionary multi-objective approach

Evolutionary Computation - Special issue on magnetic algorithms
Emergence of communication in competitive multi-agent systems: a pareto multi-objective approach

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Diversity as a selection pressure in dynamic environments

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Effects of diversity control in single-objective and multi-objective genetic algorithms

Journal of Heuristics
Using behavioral exploration objectives to solve deceptive problems in neuro-evolution

Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Improving computational mechanics optimum design using helper objectives: an application in frame bar structures

EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Helper-objective optimization strategies for the Job-Shop Scheduling Problem

Applied Soft Computing
Encouraging behavioral diversity in evolutionary robotics: An empirical study

Evolutionary Computation
Multiobjectivization with NSGA-ii on the noiseless BBOB testbed

Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Memetic algorithms and hyperheuristics applied to a multiobjectivised two-dimensional packing problem

Journal of Global Optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

A number of authors made the claim that a multiobjective approach preserves genetic diversity better than a single objective approach. Sofar, none of these claims presented a thorough analysis to the effect of multiobjective approaches. In this paper, we provide such analysis and show that a multiobjective approach does preserve reproductive diversity. We make our case by comparing a pareto multiobjective approach against a single objective approach for solving single objective global optimization problems in the absence of mutation. We show that the fitness landscape is different in both cases and the multiobjective approach scales faster and produces better solutions than the single objective approach.