estimation-of-distribution algorithm using Cauchy sampling distribution is compared with the bi-population CMA evolutionary strategy which was one of the best contenders in the black-box optimization benchmarking work...
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ISBN:
(纸本)9781450300735
estimation-of-distribution algorithm using Cauchy sampling distribution is compared with the bi-population CMA evolutionary strategy which was one of the best contenders in the black-box optimization benchmarking workshop in 2009. The results clearly indicate that the CMA evolutionary strategy is in all respects a better optimization algorithm than the Cauchy estimation-of-distribution algorithm. This paper compares both algorithms in more detail and adds to the understanding of their key features and differences.
This paper proposes a new evolutionary algorithm, called DSMGA-II, to efficiently solve optimization problems via exploiting problem substructures. The proposed algorithm adopts pairwise linkage detection and stores t...
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ISBN:
(纸本)9781450334723
This paper proposes a new evolutionary algorithm, called DSMGA-II, to efficiently solve optimization problems via exploiting problem substructures. The proposed algorithm adopts pairwise linkage detection and stores the information in the form of dependency structure matrix (DSM). A new linkage model, called the incremental linkage set, is then constructed by using the DSM. Inspired by the idea of optimal mixing, the restricted mixing and the back mixing are proposed. The former aims at efficient exploration under certain constrains. The latter aims at exploitation by refining the DSM so as to reduce unnecessary evaluations. Experimental results show that DSMGA-II outperforms LT-GOMEA and hBOA in terms of number of function evaluations on the concatenated/folded/cyclic trap problems, NK-landscape problems with various degrees of overlapping, 2D Ising spin-glass problems, and MAX-SAT. The investigation of performance comparison with P3 is also included.
Choosing a suitable algorithm from the myriads of different search heuristics is difficult when faced with a novel optimization problem. In this work, we argue that the purely academic question of what could be the be...
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Choosing a suitable algorithm from the myriads of different search heuristics is difficult when faced with a novel optimization problem. In this work, we argue that the purely academic question of what could be the best possible algorithm in a certain broad class of black-box optimizers can give fruitful indications in which direction to search for good established heuristics. We demonstrate this approach on the recently proposed DLB benchmark. Our finding that the unary unbiased black-box complexity is only O (n2) suggests the Metropolis algorithm as an interesting candidate and we prove that it solves the DLB problem in quadratic time. We also prove that better runtimes cannot be obtained in the class of unary unbiased algorithms. We therefore shift our attention to algorithms that use the information of more parents to generate new solutions and find that the significance-based compact genetic algorithm can solve the DLB problem in time O (n log n).(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons .org /licenses /by-nc -nd /4 .0/).
In this paper, a novel hybrid evolutionary algorithm combining a Hopfield net and a local search strategy is proposed to solve maximum clique problem. The algorithm makes full use of powerful searching capability of H...
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ISBN:
(纸本)9781479938414
In this paper, a novel hybrid evolutionary algorithm combining a Hopfield net and a local search strategy is proposed to solve maximum clique problem. The algorithm makes full use of powerful searching capability of Hopfield net and probabilistic statistic feature of estimation of distributionalgorithm to produce wider search in global solution domain. In particular, a possible extension way correlated with local search optimization is introduced to affect the mutation probability thus to produce guided evolution. Experiments on the popular DIMACS benchmark demonstrate that the hybrid evolutionary algorithm produces comparable and better results than other compared algorithms, including EA/G which is a state-of-the-art algorithm in the field of evolutionary computation.
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