Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Mark W. Hauschild"'
Autor:
Mark W. Hauschild, Cezary Z. Janikow
Publikováno v:
GECCO
GAs started with generic mutation and crossover operators, but over the years specialized representations and/or operators designed specifically for a given domain or problem, such as TSP, proved the most effective. In this paper, we define a class o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc1fa27f5f9bb65947d60473832252b1
https://doi.org/10.1145/3071178.3071331
https://doi.org/10.1145/3071178.3071331
Autor:
Martin Pelikan, Mark W. Hauschild
Publikováno v:
Swarm and Evolutionary Computation. 1:111-128
Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. This explicit use of probabilis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a241b6277b00ff5432f2b5890d8dd48b
https://doi.org/10.1016/j.swevo.2011.08.003
https://doi.org/10.1016/j.swevo.2011.08.003
Publikováno v:
IEEE Transactions on Evolutionary Computation. 13:1199-1217
The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper ana
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::46fa78f03f28cce40d28611acc032cf3
https://doi.org/10.1109/tevc.2008.2004423
https://doi.org/10.1109/tevc.2008.2004423
Publikováno v:
Springer Handbook of Computational Intelligence ISBN: 9783662435045
Handbook of Computational Intelligence
Handbook of Computational Intelligence
Estimation of distribution algorithms (EDA s) guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. However, EDAs are not only optimization techniques; besides the optimum or its app
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4e677f532caddf30cf2d38bd018c0f38
https://doi.org/10.1007/978-3-662-43505-2_45
https://doi.org/10.1007/978-3-662-43505-2_45
Publikováno v:
GECCO
This paper proposes a hybrid genetic algorithm to perform image segmentation based on applying the q-state Potts spin glass model to a grayscale image. First, the image is converted to a set of weights for a q-state spin glass and then a steady-state
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cf52d2f5b8f12ac92cbcb45d200800ee
https://doi.org/10.1145/2330163.2330253
https://doi.org/10.1145/2330163.2330253
Autor:
Mark W. Hauschild, Martin Pelikan
Publikováno v:
GECCO
For many optimization problems it is possible to define a distance metric between problem variables that correlates with the likelihood and strength of interactions between the variables. For example, one may define a metric so that the dependencies
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2568dd2afbb205ad789c19d329ec4c04
http://arxiv.org/abs/1201.2241
http://arxiv.org/abs/1201.2241
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783642329364
PPSN (1)
PPSN (1)
An automated technique has recently been proposed to transfer learning in the hierarchical Bayesian optimization algorithm (hBOA) based on distance-based statistics. The technique enables practitioners to improve hBOA efficiency by collecting statist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d6802f6c1f50a161eb3272b22647a91
https://doi.org/10.1007/978-3-642-32937-1_18
https://doi.org/10.1007/978-3-642-32937-1_18
Publikováno v:
GECCO
The linkage tree genetic algorithm (LTGA) identifies linkages between problem variables using an agglomerative hierarchical clustering algorithm and linkage trees. This enables LTGA to solve many decomposable problems that are difficult with more con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b2f055f0528d10b8b63a917a2f94bca
https://doi.org/10.1145/2001576.2001713
https://doi.org/10.1145/2001576.2001713
Autor:
Mark W. Hauschild, Martin Pelikan
Publikováno v:
GECCO
While different measures of problem difficulty of fitness landscapes have been proposed, recent studies have shown that many of the common ones do not closely correspond to the actual difficulty of problems when solved by evolutionary algorithms. One
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ed79f2d23e8a15c7ab0a7721a766c5af
https://doi.org/10.1145/2001576.2001662
https://doi.org/10.1145/2001576.2001662
Publikováno v:
GECCO (Companion)
Genetic Programming explores the problem search space by means of operators and selection. Mutation and crossover operators apply uniformly, while selection is the driving force for the search. Constrained GP changes the uniform exploration to pruned
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4e51647126a28a81f710e3e8a5838fbf
https://doi.org/10.1145/2001858.2002066
https://doi.org/10.1145/2001858.2002066