Climbing combinatorial fitness landscapes

Basseur, Matthieu; Goëffon, Adrien

doi:10.1016/j.asoc.2015.01.047

Cited by 25 publications

(8 citation statements)

References 24 publications

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“…Fitness landscapes with four different ruggedness properties: (A) smooth landscape, (B) locally smooth/globally rugged landscape, (C) locally rugged/globally smooth landscape, and (D) highly rugged landscape (with random heights) 38 …”

Section: Existing Chaotic Map Strategiesmentioning

confidence: 99%

Chaos teaching learning based algorithm for large‐scale global optimization problem and its application

Shukla

2021

Concurrency and Computation

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Teaching learning‐based optimization (TLBO) is a popular stochastic algorithm that has recently been widely applied in a variety of optimization problems since its start. In TLBO algorithm, the concept of chaos not only shows a vital effect in its convergence but also plays a substantial role to balance of exploration and exploitation through evolution. However, TLBO is quickly trapped in local optima and premature convergence seems when applied to sophisticated complex functions. To handle these problems, we introduced an improved TLBO algorithm using chaotic concept. To achieve ability to search for exploration and exploitation, new phase called chaotic phase is added in original TLBO algorithm. The proposed method is thoroughly evaluated on benchmark test suites. The numerical result show that proposed method is relatively effective in adapting the chaotic value regarding original TLBO in terms of solution quality and convergence rate. In addition, performance of proposed method is evaluated on benchmark KDD Cup 99 intrusion dataset. The experimental results demonstrate that proposed method achieves higher predictive accuracy, detection rate, false alarm rate, and provided more significant features as compared with other wrapper techniques.

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Section: Existing Chaotic Map Strategiesmentioning

confidence: 99%

Chaos teaching learning based algorithm for large‐scale global optimization problem and its application

Shukla

2021

Concurrency and Computation

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“…Depending on the pivot rule used by the hillclimber, e.g., first or best improvement, the structure of the basins is different, and so is its impact on search performance [2,15]. Besides local optima and their basins of attraction, a complementary view of fitness landscapes is given by the so-called local optima network (LON) [4,13].…”

Section: Preliminariesmentioning

confidence: 99%

On Stochastic Fitness Landscapes: Local Optimality and Fitness Landscape Analysis for Stochastic Search Operators

Aboutaib

Vérel

Fonlupt

et al. 2020

Lecture Notes in Computer Science

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Fitness landscape analysis is a well-established tool for gaining insights about optimization problems and informing about the behavior of local and evolutionary search algorithms. In the conventional definition of a fitness landscape, the neighborhood of a given solution is a set containing nearby solutions whose distance is below a threshold, or that are reachable using a deterministic local search operator. In this paper, we generalize this definition in order to analyze the induced fitness landscape for stochastic search operators, that is when neighboring solutions are reachable under different probabilities. More particularly, we give the definition of a stochastic local optimum under this setting, in terms of a probability to reach strictly improving solutions. We illustrate the relevance of stochastic fitness landscapes for enumerable combinatorial benchmark problems, and we empirically analyze their properties for different stochastic operators, neighborhood sample sizes, and local optimality thresholds. We also portray their differences through stochastic local optima networks, intending to gather a better understanding of fitness landscapes under stochastic search operators.

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“…The choice of the pivot directly impacts the performance of HC, either in term of solution quality, or in term of the computational cost. Recent works [40,41] highlight that first-improvement tends to be more efficient on most landscapes. The Algo.…”

Section: Hill-climbingmentioning

confidence: 99%

“…This class of algorithms often takes part in state-of-the art algorithms for solving challenging optimization problems [39,40]. HC algorithms follow the idea of gradient search by continuously improving the current solution.…”

Section: Hill-climbingmentioning

confidence: 99%

An Iterated Local Search to find many solutions of the 6-states Firing Squad Synchronization Problem

Clergue

Vérel

Formenti

2018

Applied Soft Computing

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This paper proposes an optimization approach for solving a classical problem in cellular automata theory: the 6-states Firing Squad Synchronization Problem (FSSP). To this purpose, we introduce an original optimization function which quantifies the quality of solutions according only to the main goal of the problem without taking into account any side information about cellular automata computations. This function is used for a dedicated Iterated Local Search algorithm which finds several hundreds of new solutions of the FSSP. Note, that up to present only one human-designed solution was known which is optimal in time. Most of the new solutions found by our algorithm have lower complexity (in terms of number of transitions rules used). An analysis of the fitness landscape for FSSP explains why, counter-intuitively, local search strategy can achieve good results for FSSP.

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Climbing combinatorial fitness landscapes

Cited by 25 publications

References 24 publications

Chaos teaching learning based algorithm for large‐scale global optimization problem and its application

Chaos teaching learning based algorithm for large‐scale global optimization problem and its application

On Stochastic Fitness Landscapes: Local Optimality and Fitness Landscape Analysis for Stochastic Search Operators

An Iterated Local Search to find many solutions of the 6-states Firing Squad Synchronization Problem

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