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pro vyhledávání: '"Edwin D. de Jong"'
Autor:
Edwin D. de Jong
Publikováno v:
Evolutionary Computation. 15:61-93
Coevolution has already produced promising results, but its dynamic evaluation can lead to a variety of problems that preventmost algorithms from progressing monotonically. An important open question therefore is how progress towards a chosen solutio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad3cc7080ffaee9719dc26cb97800e7a
https://doi.org/10.1162/evco.2007.15.1.61
https://doi.org/10.1162/evco.2007.15.1.61
Autor:
Edwin D. de Jong, Jordan Pollack
Publikováno v:
Evolutionary Computation. 12:159-192
In many problems of interest, performance can be evaluated using tests, such as examples in concept learning, test points in function approximation, and opponents in game-playing. Evaluation on all tests is often infeasible. Identification of an accu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d7b6fefb9c62db735c391bf5c62102b4
https://doi.org/10.1162/106365604773955139
https://doi.org/10.1162/106365604773955139
Autor:
Edwin D. de Jong, Jordan Pollack
Publikováno v:
Genetic Programming and Evolvable Machines. 4:211-233
Variable length methods for evolutionary computation can lead to a progressive and mainly unnecessary growth of individuals, known as bloat. First, we propose to measure performance in genetic programming as a function of the number of nodes, rather
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::13b6c4b752b4d57f5318ce7baaf329b6
https://doi.org/10.1023/a:1025122906870
https://doi.org/10.1023/a:1025122906870
Publikováno v:
Handbook of Natural Computing ISBN: 9783540929093
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ca53f731c6ba77ac8b72a1592bd279e
https://doi.org/10.1007/978-3-540-92910-9_31
https://doi.org/10.1007/978-3-540-92910-9_31
Autor:
Edwin D. de Jong, Peter A. N. Bosman
Publikováno v:
Studies in Computational Intelligence ISBN: 9783540762850
Fundamental research into Genetic Algorithms (GA) has led to one of the biggest successes in the design of stochastic optimization algorithms: Estimation-of-Distribution Algorithms (EDAs). These principled algorithms identify and exploit structural f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::39d4cfccd3850efbab3e80b900957abf
https://doi.org/10.1007/978-3-540-76286-7_1
https://doi.org/10.1007/978-3-540-76286-7_1
Publikováno v:
Parallel Problem Solving from Nature – PPSN X ISBN: 9783540876991
PPSN
PPSN
This paper studies the performance of four alternative evaluation methods; two instances of the Exponential Moving average, the Elo-rating and the Glicko-rating method. These methods are tested in a co-evolutionary setup using the LINT-game, which is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0bc3b021b49f8713bcf7e2904cf547b9
https://doi.org/10.1007/978-3-540-87700-4_63
https://doi.org/10.1007/978-3-540-87700-4_63
Autor:
Edwin D. de Jong, Anthony Bucci
Publikováno v:
Natural Computing Series ISBN: 9783540729631
Multiobjective Problem Solving from Nature
Multiobjective Problem Solving from Nature
We consider a class of optimization problems wherein the quality of candidate solutions is estimated by their performance on a number of tests. Classifier induction, function regression, and certain types of reinforcement learning, including problems
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb4fe88e448b1b50dd6c0a411ea3880b
https://doi.org/10.1007/978-3-540-72964-8_17
https://doi.org/10.1007/978-3-540-72964-8_17
Autor:
Ting-Shuo Yo, Edwin D. de Jong
Publikováno v:
Proceedings of the 9th annual conference on Genetic and evolutionary computation.
In this research, we compare four different evaluation methods in coevolution on the Majority Function problem. The size of the problem is selected such that evaluation against all possible test cases is feasible. Two measures are used for the compar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::692222ca45dbc2bafd62317b291a06a6
https://doi.org/10.1145/1276958.1277060
https://doi.org/10.1145/1276958.1277060
Publikováno v:
GECCO (Companion)
This tutorial is designed to introduce coevolution to those with a working familiarity with evolutionary computation. The tutorial begins by providing some basic background into what coevolution is and how it has been historically employed. The funda
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c4c408c960ba1eda43913b0431838da4
https://doi.org/10.1145/1274000.1274108
https://doi.org/10.1145/1274000.1274108
Autor:
Edwin D. de Jong
Publikováno v:
GECCO
This paper introduces the Objective Fitness Correlation, a new tool to analyze the evaluation accuracy of coevolutionary algorithms. Accurate evaluation is an essential ingredient in creating adequate coevolutionary dynamics. Based on the notion of a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::310c11f3d6b5c14b57cff14f1d31a0ff
https://doi.org/10.1145/1276958.1277055
https://doi.org/10.1145/1276958.1277055