Coevolutionary Principles

  title={Coevolutionary Principles},
  author={Elena Popovici and Anthony Bucci and R. Paul Wiegand and Edwin D. de Jong},
  booktitle={Handbook of Natural Computing},
Coevolutionary algorithms approach problems for which no function for evaluating potential solutions is present or known. Instead, algorithms rely on the aggregation of outcomes from interactions among evolving entities in order to make selection decisions. Given the lack of an explicit yardstick, understanding the dynamics of coevolutionary algorithms, judging whether a given algorithm is progressing, and designing effective new algorithms present unique challenges unlike those faced by… 

Co-Evolution Versus Evolution with Random Sampling for Acquiring Othello Position Evaluation

The results of extensive computational experiments prove that guiding coevolutionary search on the basis of games against a sample of random opponents employed by ICL has indeed a great potential when applied to the problem of Othello, and show that it is possible to design a coev evolutionary algorithm of better performance than ICL.

Investigating coevolutionary algorithms For expensive fitness evaluations in cybersecurity

This thesis devise coevolutionary algorithms and methods that achieve good results with fewer fitness evaluations, and present methods for selecting a solution to deploy after running experiments with multiple coev evolutionary algorithms.

Solving complex problems with coevolutionary algorithms

His research investigates the utility of coevolutionary methods under non-stationary environments, and uses coev evolution to facilitate the discovery of agents for reinforcement learning tasks in games such as the Arcade Learning Environment, VizDoom and Dota 2.

Conservation of Information in Coevolutionary Searches

A number of papers show that the No Free Lunch theorem does not apply to coevolutionary search. This has been interpreted as meaning that, unlike classical full query searches, coevolutionary

Novelty-Driven Cooperative Coevolution

This study shows how novelty search can be used to avoid the counterproductive attraction to stable states in coevolution, and evaluates three novelty-based approaches that rely on the novelty of the team as a whole, the noveltyof the agents’ individual behaviour, and the combination of the two.

Compensate information from multimodal dynamic landscapes: An anti-pathology cooperative coevolutionary algorithm

A multipopulation strategy is proposed to simultaneously search local or global optima in each dynamic landscape and provide them to the other components and significantly improve the rate of converging to global optimum.

Minimal criterion coevolution: a new approach to open-ended search

This paper investigates the extent to which interactions between two coevolving populations, both subject to their own constraint, or minimal criterion, can produce results that are both functional and diverse even without any behavior characterization or novelty archive.

Competitive coevolutionary algorithm decision support

Using coevolutionary algorithms to find solutions to problems is a powerful technique but once solutions are identified it can be difficult for a decision maker to select a solution to deploy.

Online Discovery of Search Objectives for Test-Based Problems

Disco is proposed, a method that automatically identifies the groups of tests for which the candidate solutions behave similarly and defines the above skills, and each such group gives rise to a derived objective, and these objectives together guide the search algorithm in multi-objective fashion.



An analysis of cooperative coevolutionary algorithms

A new view of the CCEAs is offered that includes analysis-guided suggestions for how a traditional CCEA might be modified to be better suited for optimization tasks, or might be applied to more appropriate tasks, given the nature of its dynamics.

An analysis of two-population coevolutionary computation

This dissertation is the first study that "glues" all four pieces together and provides a more holistic perspective of the field of CoEC by identifying a problem property and introducing tools for analyzing this property that are applicable across subareas.

On identifying global optima in cooperative coevolution

By modifying an existing CCEA to compare individuals using Pareto dominance, this work has produced an algorithm which reliably finds global optima and demonstrates the algorithm on two Maximum of Two Quadratics problems.

Guaranteeing Coevolutionary Objective Measures

This work presents a model of competitive fitness assessment with a single population and non-parametric selection, and shows minimum conditions and examples under which an objective measure exists, and when the dynamics of the coevolutionary algorithm are identical to those of a traditional EA.

Coevolutionary search among adversaries

New methods are described that overcome these Aaws and make coevolution more efficient, able to solve several game learning test problems that cannot be efficiently solved without them.

DECA: dimension extracting coevolutionary algorithm

The Dimension Extracting Coevolutionary Algorithm (DECA) is compared to several recent reliable coevolution algorithms on a Numbers game problem, and found to perform efficiently and application to the more realistic Tartarus problem is shown to be feasible.

Order-theoretic Analysis of Coevolution Problems: Coevolutionary Statics

A notion of solution for coevolution is defined which generalizes similar solution concepts in GA function optimization and MOO, and the ideal test set is defined, a potentially small set of tests which allow us to find the solution set of a problem.

New Methods for Competitive Coevolution

This work uses the games of Nim and 3-D Tic-Tac-Toe as test problems to explore three new techniques in competitive coevolution, which changes the way fitness is measured, shared sampling provides a method for selecting a strong, diverse set of parasites and the hall of fame encourages arms races by saving good individuals from prior generations.

An empirical analysis of collaboration methods in cooperative coevolutionary algorithms

This paper offers an empirical analysis of various types of collaboration mechanisms and presents some basic advice about how to choose a mechanism which is appropriate for a particular problem.

A Comparison of Evolutionary and Coevolutionary Search

It is shown that the increased efficacy in the coevolutionary model results from the direct exploitation of low quality strategies by the population of training cases, and there is evidence that the generality of the high-quality strategies can suffer as a result of this same exploitation.