الصفحة 1
الصفحة 1
Representations for Genetic and Evolutionary Algorithms
The book summarizes existing knowledge regarding problem representations and describes how basic properties of representations, such as redundancy, scaling, or locality, influence the performance of GEAs and other heuristic optimization methods. Using the developed theory, representations can be analyzed and designed in a theory-guided matter. The theoretical concepts are used for solving integer optimization problems and network design problems more efficiently.
Parallel problem solving from nature – PPSN XVI ; 16th International conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part II
Constitutes the refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020. The 99 revised full papers were carefully reviewed and selected from 268 submissions. The topics cover classical subjects such as automated algorithm selection and configuration; Bayesian- and surrogate-assisted optimization; benchmarking and performance measures; combinatorial optimization; connection between nature-inspired optimization and artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world applications; reinforcement learning; and theoretical aspects of nature-inspired optimization.
Parallel Problem Solving from Nature – PPSN XVI ; 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I
Constitutes the refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020. The 99 revised full papers were carefully reviewed and selected from 268 submissions. The topics cover classical subjects such as automated algorithm selection and configuration ; Bayesian- and surrogate-assisted optimization ; benchmarking and performance measures ; combinatorial optimization; connection between nature-inspired optimization and artificial intelligence ; genetic and evolutionary algorithms ; genetic programming; landscape analysis ; multiobjective optimization ; real-world applications ; reinforcement learning ; and theoretical aspects of nature-inspired optimization.
