Published on Thu Jul 02 2009

Evidence of coevolution in multi-objective evolutionary algorithms

James M Whitacre

This research contributes to the study of simulated evolution in a at least two ways. First, it establishes a broader relationship between coevolution and multi-objective optimization than has been previously considered. Second, it demonstrates that the preconditions for coevolvedary behavior are weaker than previously

0
0
0
Abstract

This paper demonstrates that simple yet important characteristics of coevolution can occur in evolutionary algorithms when only a few conditions are met. We find that interaction-based fitness measurements such as fitness (linear) ranking allow for a form of coevolutionary dynamics that is observed when 1) changes are made in what solutions are able to interact during the ranking process and 2) evolution takes place in a multi-objective environment. This research contributes to the study of simulated evolution in a at least two ways. First, it establishes a broader relationship between coevolution and multi-objective optimization than has been previously considered in the literature. Second, it demonstrates that the preconditions for coevolutionary behavior are weaker than previously thought. In particular, our model indicates that direct cooperation or competition between species is not required for coevolution to take place. Moreover, our experiments provide evidence that environmental perturbations can drive coevolutionary processes; a conclusion that mirrors arguments put forth in dual phase evolution theory. In the discussion, we briefly consider how our results may shed light onto this and other recent theories of evolution.

Wed Jun 10 2015
Neural Networks
A review of landmark articles in the field of co-evolutionary computing
Coevolution is a powerful tool in evolutionary computing that mitigates some of its endemic problems. Since its inception in 1990, there are multiple articles that have contributed greatly to the development and improvement of the coevolutionary techniques.
0
0
0
Mon Dec 14 2020
Neural Networks
Theoretical Analyses of Multi-Objective Evolutionary Algorithms on Multi-Modal Objectives
This paper takes a first step towards a deeper understanding of how evolutionary algorithms solve multi-modal multi-objective problems. We propose the OneJumpZeroJump problem, a bi-objectives problem whose single objectives are isomorphic to the classic jump functions benchmark.
0
0
0
Thu Jul 02 2009
Neural Networks
Degenerate neutrality creates evolvable fitness landscapes
The importance of evolvability, i.e. the ability to find new variants of higher fitness, is recognized in the fields of biological evolution and evolutionary computation. Understanding how systems can be designed to be evolvable is fundamental to optimization, evolution, and complex systems science.
0
0
0
Tue Dec 01 2020
Neural Networks
On Statistical Analysis of MOEAs with Multiple Performance Indicators
Assessing the empirical performance of Multi-Objective Evolutionary Algorithms (MOEAs) is vital when we extensively test a set of MOEAs. Multiple performance indicators, e.g., thegenerationational distance and the hypervolume, are frequently applied when reporting the experimental data.
0
0
0
Thu Nov 16 2006
Neural Networks
Evolutionary Optimization in an Algorithmic Setting
0
0
0
Fri Aug 06 2021
Neural Networks
Substitution of the Fittest: A Novel Approach for Mitigating Disengagement in Coevolutionary Genetic Algorithms
substitution of the fittest (SF) is a novel technique designed to counteract the problem of disengagement in two-population competitive genetic algorithms. The approach presented is domain-independent and requires no calibration.
0
0
0