Predicting Epistasis from Mathematical Models

Heckendorn, Robert B.; Whitley, Darrell

doi:10.1162/evco.1999.7.1.69

Cited by 36 publications

(41 citation statements)

References 3 publications

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“…As Heckendorn and Whitley (1999) point out, things may change, if we assume that the fitness function is given as a mathematical expression, e. g., a polynomial. But it is obvious that the optimization of polynomials of degree 2 is NP-hard (as MAX-2-SAT can be coded that way).…”

Section: Limits and Perspectives Of Fitness Function Classificationmentioning

confidence: 99%

On Classifications of Fitness Functions

Jansen

2001

Natural Computing Series

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It is well-known that evolutionary algorithms succeed to optimize some functions efficiently and fail for others. Therefore, one would like to classify fitness functions as more or less hard to optimize for evolutionary algorithms. The aim of this paper is to clarify limitations and possibilities for classifications of fitness functions from a theoretical point of view. We distinguish two different types of classifications, descriptive and analytical ones. We shortly discuss three widely known approaches, namely the N K-model, epistasis variance, and fitness distance correlation. Furthermore, we consider another recent measure, bit-wise epistasis introduced by Fonlupt, Robilliard, and Preux (1998). We discuss shortcomings and counter-examples for all four measures and use this to motivate a discussion of possibilities and limitations of classifications of fitness functions in a broader context. and find out its shortcomings.

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Section: Limits and Perspectives Of Fitness Function Classificationmentioning

confidence: 99%

On Classifications of Fitness Functions

Jansen

2001

Natural Computing Series

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“…technique to assess interdependency relationships between issues is to perform a Walsh analysis of the utility function (Heckendorn and Whitley, 1999).…”

Section: Distance From Optimummentioning

confidence: 99%

From problems to protocols: Towards a negotiation handbook

Marsa-Maestre

Klein

Jonker

et al. 2014

Decision Support Systems

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Automated negotiation protocols represent a potentially powerful tool for problem solving in decision support systems involving participants with conflicting interests. However, the effectiveness of negotiation approaches depends greatly on the negotiation problem under consideration. Since there is no one negotiation protocol that clearly outperforms all others in all scenarios, we need to be able to decide which protocol is most suited for each particular problem. The goal of our work is to meet this challenge by defining a "negotiation handbook", that is, a collection of design rules which allow us, given a particular negotiation problem, to choose the most appropriate protocol to address it. This paper describes our progress towards this goal, including a tool for generating a wide range of negotiation scenarios, a set of high-level metrics for characterizing how negotiation scenarios differ, a testbed environment for evaluating protocol performance with different scenarios, and a community repository which allows us to systematically record and analyze protocol performance data.

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“…By the Balanced Sum Theorem for Hyperplanes [HW99] the sum is 2 bc(m) if j ⊆ m which is the same as m ⊆ j and is 0 otherwise.…”

Section: Theorem 2 (Walsh Function Probing) For Any J M ∈ B and C ∈mentioning

confidence: 99%

“…The more units, or positions, that simultaneously interact (the higher the epistasis) the greater the degree of freedom to "hide" the optimum anywhere in the subdomain formed by the interacting units [HW99]. High epistasis, however, is no guarantee of a difficult problem.…”

Section: Introductionmentioning

confidence: 99%

Efficient Linkage Discovery by Limited Probing

Heckendorn

Wright

2003

Genetic and Evolutionary Computation — GECCO 2003

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Abstract. This paper addresses the problem of determining the epistatic linkage of a function from binary strings to the reals. There is a close relationship between the Walsh coefficients of the function and "probes" (or perturbations) of the function. This relationship leads to two linkage detection algorithms that generalize earlier algorithms of the same type. A rigorous complexity analysis is given of the first algorithm. The second algorithm not only detects the epistatic linkage, but also computes all of the Walsh coefficients. This algorithm is much more efficient than previous algorithms for the same purpose.

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Predicting Epistasis from Mathematical Models

Cited by 36 publications

References 3 publications

On Classifications of Fitness Functions

On Classifications of Fitness Functions

From problems to protocols: Towards a negotiation handbook

Efficient Linkage Discovery by Limited Probing

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