An agent-based model of hierarchic genetic search

Schaefer, Robert; Byrski, Aleksander; Kołodziej, Joanna; Smółka, Maciej

doi:10.1016/j.camwa.2012.02.052

Cited by 15 publications

(23 citation statements)

References 8 publications

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“…Moreover, the agent based synchronization among demes was applied for island model for which the Markov chain model was established and its ergodicity was proven (see [54]). The Markov chain model was also obtained for the complex, metaheuristic HGS with a tree-structured set of demes (see [55]). …”

Section: Introductionmentioning

confidence: 99%

Asymptotic guarantee of success for multi-agent memetic systems

Byrski¹,

Schaefer²,

Smółka³

et al. 2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. The paper introduces a stochastic model for a class of population-based global optimization meta-heuristics, that generalizes existing models in the following ways. First of all, an individual becomes an active software agent characterized by the constant genotype and the meme that may change during the optimization process. Second, the model embraces the asynchronous processing of agent's actions. Third, we consider a vast variety of possible actions that include the conventional mixing operations (e.g. mutation, cloning, crossover) as well as migrations among demes and local optimization methods. Despite the fact that the model fits many popular algorithms and strategies (e.g. genetic algorithms with tournament selection) it is mainly devoted to study memetic algorithms.The model is composed of two parts: EMAS architecture (data structures and management strategies) allowing to define the space of states and the framework for stochastic agent actions and the stationary Markov chain described in terms of this architecture. The probability transition function has been obtained and the Markov kernels for sample actions have been computed.The obtained theoretical results are helpful for studying metaheuristics conforming to the EMAS architecture. The designed synchronization allows the safe, coarse-grained parallel implementation and its effective, sub-optimal scheduling in a distributed computer environment. The proved strong ergodicity of the finite state Markov chain results in the asymptotic stochastic guarantee of success, which in turn imposes the liveness of a studied metaheuristic. The Markov chain delivers the sampling measure at an arbitrary step of computations, which allows further asymptotic studies, e.g. on various kinds of the stochastic convergence.

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Section: Introductionmentioning

confidence: 99%

Asymptotic guarantee of success for multi-agent memetic systems

Byrski¹,

Schaefer²,

Smółka³

et al. 2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

View full text Add to dashboard Cite

show abstract

“…The realnumber HGS and its efficiency were discussed by Wierzba et al [83] and by Schaefer and Barabasz [29]. The complete Markov model of HGS dynamics (excluding branch reduction mechanism) was described by Schaefer et al [59].…”

Section: Common Adaptation Of Accuracy and Deme Structure -Hgsmentioning

confidence: 99%

“…Since the description of the model is rather complicated, we recall here only some of its elements in a form that is simplified but suitable for derivation of the first hitting time for this strategy. See the work of Schaefer et al [59] for the general formulation and all necessary details.…”

Section: Generalization Of Fht Evaluation For Hierarchic Strategiesmentioning

confidence: 99%

“…The formulation of the HGS dynamics Markov model [59] gives us the opportunity to compute the first hitting time for this complex strategy as well. Since the description of the model is rather complicated, we recall here only some of its elements in a form that is simplified but suitable for derivation of the first hitting time for this strategy.…”

Section: Generalization Of Fht Evaluation For Hierarchic Strategiesmentioning

confidence: 99%

See 1 more Smart Citation

On the Computational Cost and Complexity of Stochastic Inverse Solvers

Faliszewski

Smółka

Schaefer

et al. 2016

csci

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“…Another related work is that of Schaefer et al (2012) on hierarchical genetic search (HGS), which is-similarly to our approach-also a way of running multiple solver instances concurrently. However, the details of the two approaches are quite different, given that HGS operates with genetic algorithms whereas we use an exact algorithm for constraint satisfaction as the base solver.…”

mentioning

confidence: 99%

Accelerating backtrack search with a best-first-search strategy

Mann

Szép

2014

International Journal of Applied Mathematics and Computer Science

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Backtrack-style exhaustive search algorithms for NP-hard problems tend to have large variance in their runtime. This is because "fortunate" branching decisions can lead to finding a solution quickly, whereas "unfortunate" decisions in another run can lead the algorithm to a region of the search space with no solutions. In the literature, frequent restarting has been suggested as a means to overcome this problem. In this paper, we propose a more sophisticated approach: a best-firstsearch heuristic to quickly move between parts of the search space, always concentrating on the most promising region. We describe how this idea can be efficiently incorporated into a backtrack search algorithm, without sacrificing optimality. Moreover, we demonstrate empirically that, for hard solvable problem instances, the new approach provides significantly higher speed-up than frequent restarting.

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An agent-based model of hierarchic genetic search

Cited by 15 publications

References 8 publications

Asymptotic guarantee of success for multi-agent memetic systems

Asymptotic guarantee of success for multi-agent memetic systems

On the Computational Cost and Complexity of Stochastic Inverse Solvers

Accelerating backtrack search with a best-first-search strategy

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