Spacing memetic algorithms

Porumbel, Daniel Cosmin; Hao, Jin‐Kao; Kuntz, Pascale

doi:10.1145/2001576.2001720

Cited by 8 publications

(7 citation statements)

References 21 publications

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“…In this section we will test our analyzed algorithms on the Maximum Clique Problem. To avoid only comparing algorithms that are analyzed in theory, we also bring a state-ofart Spacing Memetic Algorithm (SMA) [18] into comparison.…”

Section: Resultsmentioning

confidence: 99%

“…In Section IV, we show that if a graph needs an exponential time to escape a local optimal clique, the (1+1) AMA with RPLS will be drastically faster to escape than the basic (1+1) EA. In Section V, experimental results provides a running time comparison among the (1+1) EA [4], the Dynamic (1+1) EA [10], (1+1) MA [22], the (1+1) AMA [2], and the SMA [18] on the Maximum Clique Problem. Our conclusions and future work will be given in Section VI.…”

Section: Introductionmentioning

confidence: 99%

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A (1+1) Adaptive Memetic Algorithm for the Maximum Clique Problem

Dinneen

Wei

2013

2013 IEEE Congress on Evolutionary Computation

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A memetic algorithm (MA) is an Evolutionary Algorithm (EA) augmented with a local search. We previously defined a (1+1) Adaptive Memetic Algorithm (AMA) with two different local searches, and the comparison with the well-known (1+1) EA, Dynamic (1+1) EA and (1+1) MA on some toy functions showed promise for our proposed algorithm.In this paper we focus on the NP-hard Maximum Clique Problem, and show the success of our proposed (1+1) AMA. We propose a new metric (expected running time to escape a local optimal), and show how this metric dominates the expected running time of finding a maximum clique. Then based on this new metric, we show the above analyzed algorithms are expected to find a maximum clique on graphs, bipartite graphs and sparse random graphs in a polynomial time in the number of vertices. Also based on our new metric, we will show that if an algorithm takes an exponential time to find a maximum clique of a graph, it must have been trapped into at least one local optimal that is extremely hard to escape. Furthermore, we will show that our proposed (1+1) AMA with a random permutation local search is expected to escape these (hard to escape) local optimal cliques drastically faster than the well-known basic (1+1) EA. The success of our experimental results also shows the benefit of our adaptive strategy combined with the random permutation local search.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A (1+1) Adaptive Memetic Algorithm for the Maximum Clique Problem

Dinneen

Wei

2013

2013 IEEE Congress on Evolutionary Computation

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“…When an offspring is generated an elimination rule is used to select a member of the population and replace it with the new offspring. In general, one has to take both into consideration, the fitness of an individual and the distance between individuals in the population [35] in order to avoid premature convergence of the algorithm. We evict the solution that is most similar with respect to the edges that run in between clusters with the offspring among those individuals in the population that have a worse or equal objective than the offspring itself.…”

Section: Memetic Graph Clusteringmentioning

confidence: 99%

Memetic Graph Clustering

Biedermann,

Henzinger,

Schulz

et al. 2018

Preprint

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It is common knowledge that there is no single best strategy for graph clustering, which justifies a plethora of existing approaches. In this paper, we present a general memetic algorithm, VieClus, to tackle the graph clustering problem. This algorithm can be adapted to optimize different objective functions. A key component of our contribution are natural recombine operators that employ ensemble clusterings as well as multi-level techniques. Lastly, we combine these techniques with a scalable communication protocol, producing a system that is able to compute high-quality solutions in a short amount of time. We instantiate our scheme with local search for modularity and show that our algorithm successfully improves or reproduces all entries of the 10th DIMACS implementation challenge under consideration using a small amount of time.

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“…However, the partition distance [27,59] conveys a more direct and semantic sense of distance and it is often used in coloring papers for various purposes [20,22,26,29,43,57]. A simple offspring rejection rule based on this metric is presented in [57] and it seems that the approach can be generalized to other problems as well [58]. Another point where one can intervene is the elimination step.…”

Section: Other Evolutionary Advances and Algorithmsmentioning

confidence: 99%

Recent Advances in Graph Vertex Coloring

Galinier

Hamiez

Hao

et al. 2013

Handbook of Optimization

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Graph vertex coloring is one of the most studied NP-hard combinatorial optimization problems. Given the hardness of the problem, various heuristic algorithms have been proposed for practical graph coloring, based on local search, population-based approaches and hybrid methods. The research in graph coloring heuristics is very active and improved results have been obtained recently, notably for coloring large and very large graphs. This chapter surveys and analyzes graph coloring heuristics with a focus on the most recent advances.

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Spacing memetic algorithms

Cited by 8 publications

References 21 publications

A (1+1) Adaptive Memetic Algorithm for the Maximum Clique Problem

A (1+1) Adaptive Memetic Algorithm for the Maximum Clique Problem

Memetic Graph Clustering

Recent Advances in Graph Vertex Coloring

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