The No Free Lunch and Realistic Search Algorithms

ABSTRACT The No-Free-Lunch theorems (NFLTs) are criticized for being too general to be of any relevance to the real world scenario. This paper investigates, both formally and empirically, the implications of the NFLTs for realistic search algorithms. In the first part of the paper, by restricting ourselves to a specific performance measure, we derive a new NFL result for a class of problems which is not closed under permutations. In the second part, we discuss properties of this set which are likely to be true for realistic search algorithms. We provide empirical support for this i

From Likert scales to images: Validating a novel creativity measure with image based response scales.

The use of image-based testing to assess individual differences has increased substantially in recent years, with proponents arguing that they offer a more engaging alternative to text-based psychometric tests. Yet research examining the validity of these tests is near to non-existent. Traditional image-based formats have been little more than an adaptation of self-reports, with images replacing questions but not response options. The current study develops a novel image-based creativity measure, where images replace conventional response scales, and scores on the measures are obtained using a linear regression scoring algorithm to predict three self-reported creativity measures. Using sequential forward selection on a set of 77 image-based items, an optimal solution of 14 items that were valid predictors of self-reported creativity scores were identified. The image-based measure had good test-retest reliability. Implications are discussed in terms of the usefulness of image-based testing for practitioners seeking engaging and short test formats

Problem hardness for randomized search heuristics with comparison-based selection : a focus on evolutionary algorithms

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General Terms Algorithms, Performance, Theory.

In [20] we introduced a new concept of a landscape: the information landscape. We showed that for problems of very small size (e.g. a 3-bit problem), it can be used to generally and accurately predict the performance of a GA. Based on this framework, in this paper we develop a method to predict GA hardness on realistic landscapes. We give empirical results which support our approach. Categories and Subject Descriptor

Information Landscapes and the Analysis of Search Algorithms

In [15] we introduced the information landscape as a new concept of a landscape. We showed that for a landscape of a small size, information landscape theory can be used to predict the performance of a GA without running the algorithm. Based on this framework, here we develop a new theoretical model to study search algorithms in general. Particularly, we are able to infer important properties of a search algorithm without having knowledge about its specific operators. We give an example of this technique for a simple GA

Theory and principled methods for the design of metaheuristics

Metaheuristics, and evolutionary algorithms in particular, are known to provide efficient, adaptable solutions for many real-world problems, but the often informal way in which they are defined and applied has led to misconceptions, and even successful applications are sometimes the outcome of trial and error. Ideally, theoretical studies should explain when and why metaheuristics work, but the challenge is huge: mathematical analysis requires significant effort even for simple scenarios and real-life problems are usually quite complex.  In this book the editors establish a bridge between the