GA-Hardness Revisited

Guo, Haipeng; Hsu, William H.

doi:10.1007/3-540-45110-2_36

Cited by 8 publications

(9 citation statements)

References 15 publications

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“…that runs in polynomial-time, cannot exist (unless P = NP or BPP 1 = NP). The general agreement in literature seems to be that no satisfactory problem difficulty measure for search heuristics has been found [30,23,24].…”

Section: Introductionmentioning

confidence: 60%

“…As pointed out by Guo and Hsu [23] "Any efforts like this are doomed to fail", because the class of GA algorithms is too broad. The same problem can change from a hard problem to an easy problem by changing the GA settings.…”

Section: Introductionmentioning

confidence: 99%

“…Many attempts at characterising optimisation problems have focused on finding a measure that could divide problems into those that are easy and those that are hard to solve [30,48,23]. These attempts have not been very successful.…”

Section: Introductionmentioning

confidence: 99%

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A survey of techniques for characterising fitness landscapes and some possible ways forward

Malan

Engelbrecht

2013

Information Sciences

231

115

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Section: Introductionmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

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A survey of techniques for characterising fitness landscapes and some possible ways forward

Malan

Engelbrecht

2013

Information Sciences

231

115

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“…This is not a new problem or idea. Many have tried to predict problem hardness and many have failed, leading to the conclusion that no satisfactory problem difficulty measure for search heuristics has been found [20,26]. In fact, trying to find a computationally feasible hardness measure is a futile exercise, since He et al [21] have proved that a predictive version of such a measure, i.e.…”

Section: Introductionmentioning

confidence: 97%

Fitness Landscape Analysis for Metaheuristic Performance Prediction

Malan

Engelbrecht

2014

Emergence, Complexity and Computation

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Abstract. Metaheuristics have become popular for solving complex optimisation problems where classical techniques are either infeasible or perform poorly. Despite many success stories, it is well known that metaheuristics sometimes fail and that researchers and practitioners frequently resort to trial and error to find an appropriate algorithm or setting to solve a given problem. Within the framework of the general algorithm selection problem, this chapter addresses the feasibility of predicting algorithm performance on unknown real-valued problems based on fitness landscape features. Normalized metrics are proposed for quantifying algorithm performance on known problems to generate suitable training data. Performance metrics are tested using a standard particle swarm optimisation algorithm and are investigated alongside three existing fitness landscape measures. This preliminary investigation highlights the need for a shift in focus away from predicting general problem hardness towards characterising problems where each fitness landscape technique has value as a part-predictor of algorithm performance.

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“…NK landscapes [9], [10] use the same idea (epistasis) in order to create an artificial, arbitrary, landscape with a tunable degree of difficulty. These attempts, too, didn't succeed in giving a full explanation of the behavior of a GA [6], [11], [12].…”

Section: Introductionmentioning

confidence: 99%