Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Jan Paredis"'
Autor:
Jan Paredis
Publikováno v:
CEC
2017 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 279-285
STARTPAGE=279;ENDPAGE=285;TITLE=2017 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
2017 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 279-285
STARTPAGE=279;ENDPAGE=285;TITLE=2017 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
This paper investigates the evolution of two types of simple Genotype Phenotype Mappings (GPMs): a many-to-one mapping and a one-to-many mapping. Both GPMs are under genetic control. For both types of mappings different Regions Of Maximum Adaptabilit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa79ac045bd43917d7055e2837888da4
https://doi.org/10.1109/cec.2017.7969324
https://doi.org/10.1109/cec.2017.7969324
Publikováno v:
IEEE Congress on Evolutionary Computation, 201-208
STARTPAGE=201;ENDPAGE=208;TITLE=IEEE Congress on Evolutionary Computation
CEC
STARTPAGE=201;ENDPAGE=208;TITLE=IEEE Congress on Evolutionary Computation
CEC
This paper develops a new method for coevolution, named Fitness-Diversity Driven Coevolution (FDDC). This approach builds on existing methods by a combination of a (predator-prey) Coevolutionary Genetic Algorithm (CGA) and novelty search. The innovat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6ed045a3c92ca90595aef85e385053c6
https://cris.maastrichtuniversity.nl/en/publications/c15870c7-aa80-4d3e-925d-c94b8518aaf9
https://cris.maastrichtuniversity.nl/en/publications/c15870c7-aa80-4d3e-925d-c94b8518aaf9
Autor:
Ton Weijters, Jan Paredis
Publikováno v:
Knowledge-Based Systems. 15:85-94
Lists of if–then rules (i.e. ordered rule sets) are among the most expressive and intelligible representations for inductive learning algorithms. Two extreme strategies searching for such a list of rules can be distinguished: (i) local strategies p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c9cafac2b376ae7bf76b42db302a98a
https://doi.org/10.1016/s0950-7051(01)00124-1
https://doi.org/10.1016/s0950-7051(01)00124-1
Autor:
Jan Paredis
Publikováno v:
Parallel Problem Solving from Nature ISBN: 3540541489
PPSN
PPSN
This paper describes research on the use of explicitly programmable complex dynamical systems as a high-level parallel programming methodology. We show how problem solving can be seen as two processes interacting at a micro-level, namely selection an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7046c2457bdc054830b18eb8f7a4ed84
https://doi.org/10.1007/bfb0029783
https://doi.org/10.1007/bfb0029783
Autor:
Jan Paredis
Publikováno v:
Handbook of Evolutionary Computation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f0be52a7278afd91343f8f713a311da
https://doi.org/10.1887/0750308958/b386c88
https://doi.org/10.1887/0750308958/b386c88
Autor:
Jan Paredis
Publikováno v:
Evolutionary Computation 2 ISBN: 9780750306652
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5213ca1484b8e2ce62d24aef17d32a88
https://doi.org/10.1201/9781420034349.ch23
https://doi.org/10.1201/9781420034349.ch23
Autor:
Jan Paredis
Publikováno v:
Parallel Problem Solving from Nature PPSN VI ISBN: 9783540410560
PPSN
PPSN
This paper shows that the performance of coevolutionary genetic algorithms can be improved considerably by introducing a balancing mechanism. This is to prevent one population from "out-evolving" the other one. As a result, fitness variance is mainta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1803a7d9cb13a109a0e987a9c9f405ed
https://doi.org/10.1007/3-540-45356-3_49
https://doi.org/10.1007/3-540-45356-3_49
Autor:
James Cohoon, David Beasley, Robert L. Smith, Thyagarajan Sadasivan, C Karr, Peter Nordin, Devaraya Prabhu, Jan Paredis, James M. Varanelli, Charles Campbell Palmer, A. Schultz, J. David Schaffer, Hitoshi Iba, Donald H. Kraft, Bill P. Buckles, Rajarshi Das, Aaron Kershenbaum, B. A. Dike, Peter Willett, James P. Crutchfield, Frederick E. Petry, William J. Martin, E Howard Oakley, Melanie Mitchell, Kenneth A. De Jong, Cezary Janikow, John J. Grefenstette, Terence C. Fogarty
Publikováno v:
Handbook of Evolutionary Computation ISBN: 9780750308953
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ab4fc550b5281e799eed96c4c03050d5
https://doi.org/10.1201/9781420050387.ptg
https://doi.org/10.1201/9781420050387.ptg
Autor:
Jan Paredis
Publikováno v:
Parallel Problem Solving from Nature — PPSN IV ISBN: 9783540617235
PPSN
PPSN
This work studies the interaction of evolution and learning. It starts from the coevolutionary genetic algorithm (CGA) introduced earlier. Two techniques — life-time fitness evaluation (LTFE) and predator-prey coevolution — boost the genetic sear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::81b5baa0af5840a299af22403ad64007
https://doi.org/10.1007/3-540-61723-x_971
https://doi.org/10.1007/3-540-61723-x_971
Autor:
Jan Paredis
Publikováno v:
Artificial life. 2(4)
This article proposes a general framework for the use of coevolution to boost the performance of genetic search. It combines coevolution with yet another biologically inspired technique, called lifetime fitness evaluation (LTFE). Two unrelated proble
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3991ab70a4bd562ea3bb2912fa936f80
https://pubmed.ncbi.nlm.nih.gov/8942053
https://pubmed.ncbi.nlm.nih.gov/8942053