Fundamental Limitations on Search Algorithms: Evolutionary Computing in Perspective

  title={Fundamental Limitations on Search Algorithms: Evolutionary Computing in Perspective},
  author={Nicholas J. Radcliffe and Patrick D. Surry},
  booktitle={Computer Science Today},
The past twenty years has seen a rapid growth of interest in stochas- tic search algorithms, particularly those inspired by natural processes in physics and biology. Impressive results have been demonstrated on complex practical op- timisation problems and related search applications taken from a variety of fields, but the theoretical understanding of these algorithms remains weak. This results partly from the insufficient attention that has been paid to results showing certain fundamental… 

Demonstration of effective global optimization techniques via comparative analysis on a large analytical problem set

This work treats algorithms as sequential sampling algorithms, and groups them by sampling scheme, implying greater overall compatibility than other groups, and scale much better than other group on 2nd and 4th order polynomials up to 100-dimensions.

Coevolutionary free lunches

This paper presents a general framework covering most optimization scenarios and shows that in self-play there are free lunches: in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems.

Representation Issues in Neighborhood Search and Evolutionary Algorithms

Some very general properties of representations as they relate to neighborhood search methods are explored and the number of local optima under a neighborhood search operator for standard Binary and standard binary reeected Gray codes is developed and explored as one measure of problem complexity.

When and why metaheuristics researchers can ignore “No Free Lunch” theorems

An argument against a common paraphrase of NFL findings—that algorithms must be specialised to problem domains to do well—after problematising the usually undefined term “domain” is presented, which offers a novel view of the real meaning of NFL.

Formal Search Algorithms + Problem Characterisations = Executable Search Strategies

This paper presents a method for specifying algorithms with respect to abstract or formal representations, making them independent of both problem domain and representation, and defines a procedure for generating an appropriate problem representation from an explicit characterisation of a problem domain that captures beliefs about its structure.

A note on research methodology and benchmarking optimization algorithms

The intention of this paper is to summarize the literature related to benchmarking optimization algorithms, with a focus on benchmarking in the face of the "no free lunch" theorem, and useful statistical tools for interpreting results.

Random Heuristic Search

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No Free Lunch Theorems: Limitations and Perspectives of Metaheuristics

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  • Computer Science
    Theory and Principled Methods for the Design of Metaheuristics
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It is not likely that the preconditions of the NFL theorems are fulfilled for a problem class and thus differences between algorithms exist, therefore, tailored algorithms can exploit structure underlying the optimization problem.

Scientific discovery as a combinatorial optimisation problem: How best to navigate the landscape of possible experiments?

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    BioEssays : news and reviews in molecular, cellular and developmental biology
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A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed



No Free Lunch Theorems for Search

It is shown that all algorithms that search for an extremum of a cost function perform exactly the same, when averaged over all possible cost functions, which allows for mathematical benchmarks for assessing a particular search algorithm's performance.

Handbook Of Genetic Algorithms

This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.

An Overview of Evolutionary Algorithms for Parameter Optimization

Three main streams of evolutionary algorithms (EAs), probabilistic optimization algorithms based on the model of natural evolution, are compared in this article: evolution strategies (ESs),

A Model of Landscapes

A model of landscapes that is general enough to encompass most of what computer scientists would call search, though the model is not restricted to either the field or the viewpoint is presented.

Epistasis Variance: Suitability of a Representation to Genetic Algorithms

By viewing the representation as a whole, being more than the sum of its composing parts, the discussion on epistasis in GAs reveals several fundamental features of GAs and leads to a unique mechanism for "spying" on the suitability of a representation to a GA.

The Evolution Strategy. A Mathematical Model of Darwinian Evolution

It is assumed that evolution, during its action over more than a thousand million years, gave itself an optimal mode of operation and the imitation of rules of biological evolution should yield an excellent experimental method in engineering to design better technical apparatus.

Future paths for integer programming and links to artificial intelligence

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The Role of Development in Genetic Algorithms

Pareto Optimality, Ga-easiness and Deception

Insight is provided into the kind of spaces where recombination is necessary suggesting further study of properties of such spaces, and what it means to be GA{easy and hill{climbing hard.

Adaptation in natural and artificial systems

Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.