A Puzzle-Based Genetic Algorithm with Block Mining and Recombination Heuristic for the Traveling Salesman Problem

Chang, Pei‐Chann; Huang, Wei-Hsiu; Zhang, Zhenzhen

doi:10.1007/s11390-012-1275-3

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…We see that SDGP is comparable to p-ACGA [16] in terms of its performance. However, the GA part of p-ACGA was run for 50n generations with a population size of 100, which avails to, e.g., 50 * 51 * 100 = 255 000 evaluated candidate solutions for the small-scale problem eil51, as opposed to the 16 384 development steps in total used by SDGP for each problem.…”

Section: Tsp Instance Featuresmentioning

confidence: 77%

“…Two hybrid algorithms that combine permutation-based GAs with ACO are the hybrid-GA [15] and the p-ACGA [16]. Whereas hybrid-GA utilizes a pheromone matrix to improve its crossover operator, p-ACGA uses ACO to mine a set of good building blocks that can be injected into a population as artificial genotypes.…”

Section: A Related Work On Tspsmentioning

confidence: 99%

“…The population of our GP procedure holds λ = 64 individuals p, which evolve according to a (μ, λ) scheme (μ being 16). Each individual holds a genotype p.g ∈ G, which is a mathematical function in the well-known tree representation from symbolic regression [5][6][7].…”

Section: Genetic Programmingmentioning

confidence: 99%

“…16 best individuals according to fitness v , discard the rest. c) Create λ = 64 new individuals (maximum tree depth 8) by using i) sub-tree exchange crossover (with probability 0.25).ii) sub-tree replacement mutation (with probability 0.75).…”

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confidence: 99%

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SDGP: A developmental approach for traveling salesman problems

Ouyang

Weise

Devert

et al. 2013

2013 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS)

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This paper presents an Evolutionary Algorithm using a new ontogenic approach, called Staged Developmental Genetic Programming (SDGP), for solving symmetric Traveling Salesman Problems (TSPs). In SDGP, a genotype-phenotype mapping (gpm) is used to refine candidate solutions to a TSP -these candidate solutions are represented as permutations. The gpm performs several development steps, in each of which such a permutation x is incrementally modified. In each iteration within a development step, the process can choose to either apply one of seven different modifications to a specific section of x or do nothing. The choice is made by the genotypes g, which are functions assigning real-valued ratings to the possible modifications. Smaller ratings are better and the best-rated modification is then applied, if its rating is lower than a given threshold. The genotypes are evolved using tree-based Genetic Programming. Comprehensive numerical simulation experiments show that our proposed algorithm scales well with the problem size and delivers competitive results compared to other state-ofthe-art approaches in the TSP literature.

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Section: Tsp Instance Featuresmentioning

confidence: 77%

Section: A Related Work On Tspsmentioning

confidence: 99%