Comparing interactive evolutionary multiobjective optimization methods with an artificial decision maker

Autor: ANA B. RUIZ, Bekir Afsar, Kaisa Miettinen

Publikováno v: Complex & Intelligent Systems. 9:1165-1181

Solving multiobjective optimization problems with interactive methods enables a decision maker with domain expertise to direct the search for the most preferred trade-offs with preference information and learn about the problem. There are different i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77d17dcaaa2f9c74b195dd0656f41c4e
https://doi.org/10.1007/s40747-021-00586-5

Impact of chaotic dynamics on the performance of metaheuristic optimization algorithms : An experimental analysis

Autor: Quoc Bao Diep, Nikolai V. Kuznetsov, Giacomo Innocenti, Vaclav Snasel, Alberto Tesi, Ivan Zelinka, Swagatam Das, Fabio Schoen

Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin's theory of evolution as well as Mendel's theory of genetic heritage. In this paper, we debate whether pseudo-ra

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86c16b3be900d8316accda0d9caa4ad9
http://urn.fi/URN:NBN:fi:jyu-202201241254

Diverse partner selection with brood recombination in genetic programming

Autor: Asoke K. Nandi, Muhammad Waqar Aslam, Zhechen Zhu

Publikováno v: Applied Soft Computing. 67:558-566

The ultimate goal of learning algorithms is to find the best solution from a search space without testing each and every solution available in the search space. During the evolution process new solutions (children) are produced from existing solution

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::805840d18992badc5de7f2f009608286
https://doi.org/10.1016/j.asoc.2018.03.035

A Feature Rich Distance-Based Many-Objective Visualisable Test Problem Generator

Autor: Tinkle Chugh, Richard Allmendinger, Kaisa Miettinen, Jonathan E. Fieldsend

Publikováno v: Fieldsend, J, Chugh, T, Allmendinger, R & Miettinen, K 2019, A Feature Rich Distance-Based Many-Objective Visualisable Test Problem Generator . in GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference . The Genetic and Evolutionary Computation Conference, Prague, Czech Republic, 13/07/19 . https://doi.org/10.1145/3321707.3321727
GECCO

In optimiser analysis and design it is informative to visualise how a search point/population moves through the design space over time. Visualisable distance-based many-objective optimisation problems have been developed whose design space is in two-

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::233d524b45208bbdd9994f644d37d233
https://pure.manchester.ac.uk/ws/files/87967444/From_multi_to_many_objective_optimization_with_objectives_of_non_uniform_latencies.pdf

Towards Better Integration of Surrogate Models and Optimizers

Autor: Martin Zaefferer, Tinkle Chugh, Alma A. M. Rahat, Vanessa Volz

Publikováno v: High-Performance Simulation-Based Optimization ISBN: 9783030187637
High-Performance Simulation-Based Optimization

Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been proven to be very effective in solving (synthetic and real-world) computationally expensive optimization problems with a limited number of function evaluations. The two main components of S

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a196139da557fa95b755b5bb9fd56682
https://doi.org/10.1007/978-3-030-18764-4_7