Skip to search formSkip to main contentSkip to account menu

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.
View on ACM (opens in a new tab)

Automated Extraction of Problem Structure

This work investigates how the notion of problem structure can be made precise, and proposes a formal definition that is applicable to problems in which the quality of candidate solutions is evaluated by means of a series of tests.
View via Publisher (opens in a new tab)

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.
View on ACM (opens in a new tab)

Coevolutionary Principles

This chapter outlines the ends and means of coevolutionary algorithms: what they are meant to find, and how they should find it.
View via Publisher (opens in a new tab)

A Mathematical Framework for the Study of Coevolution

A theoretical framework for studying coevolution based on the mathematics of ordered sets is presented, generalizing the notion of Pareto non-dominated front from the field of multi-objective optimization and showing, in the special case of two-player games, that Pare to dominance is closely related to intransitivities in the game.

Objective Set Compression

A method for reducing the number of dimensions without sacrificing the favorable properties of the multiobjective approach is examined, which is a form of dimension extraction and finds underlying objectives implicit in test-based problems.
View via Publisher (opens in a new tab)

Emergent geometric organization and informative dimensions in coevolutionary algorithms

It is argued that when entities are only incented to perform well, and adaptation of the function of measurement is neglected, algorithms tend not to keep informative dimensions and thus fail to produce high-performing entities.

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.

A Data-Driven Analysis of Informatively Hard Concepts in Introductory Programming

DECA inspired by coevolution and co-optimization theory is applied to analyze the data collected by software tutors called problets used by introductory programming students in Spring 2014 and the results are presented, i.e., informatively easy/hard concepts on a dozen different topics covered in a typical introductory programming course.
View on ACM (opens in a new tab)

EvoParsons: design, implementation and preliminary evaluation of evolutionary Parsons puzzle

A generation-by-generation detailed analysis of the evolving population of Parsons puzzles confirms the occurrence of incremental improvements that can be explained in pedagogical terms.
View on Springer (opens in a new tab)
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)