Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

Introduction to Optimization The Binary Genetic Algorithm The Continuous Parameter Genetic Algorithm Applications An Added Level of Sophistication Advanced Applications Evolutionary Trends Appendix Glossary Index.

https://cdn.preterhuman.net/texts/science_and_technology/nature_and_biology/Bioinformatics/Practical%20Genetic%20Algorithms%20-%20Randy%20L.%20Haupt,%20Sue%20Ellen%20Haupt.pdf

Practical Genetic Algorithms

This paper puts forward two useful methods for self-adaptation of the mutation distribution - the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the self-adaptation of arbitrary (normal) mutation distributions are developed. Applying arbitrary, normal mutation distributions is equivalent to applying a general, linear problem encoding. The underlying objective of mutative strategy parameter control is roughly to favor previously selected mutation steps in the future. If this objective is pursued rigorously, a completely derandomized self-adaptation scheme results, which adapts arbitrary normal mutation distributions. This scheme, called covariance matrix adaptation (CMA), meets the previously stated demands. It can still be considerably improved by cumulation - utilizing an evolution path rather than single search steps. Simulations on various test functions reveal local and global search properties of the evolution strategy with and without covariance matrix adaptation. Their performances are comparable only on perfectly scaled functions. On badly scaled, non-separable functions usually a speed up factor of several orders of magnitude is observed. On moderately mis-scaled functions a speed up factor of three to ten can be expected.

/pdf/completely-derandomized-self-adaptation-in-evolution-4berz8v4fr.pdf

Completely Derandomized Self-Adaptation in Evolution Strategies

Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EAs; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.

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Evolving artificial neural networks

The clonal selection principle is used to explain the basic features of an adaptive immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens (Ag's) are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective Ag's. This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. The general algorithm, named CLONALG, is derived primarily to perform machine learning and pattern recognition tasks, and then it is adapted to solve optimization problems, emphasizing multimodal and combinatorial optimization. Two versions of the algorithm are derived, their computational cost per iteration is presented, and a sensitivity analysis in relation to the user-defined parameters is given. CLONALG is also contrasted with evolutionary algorithms. Several benchmark problems are considered to evaluate the performance of CLONALG and it is also compared to a niching method for multimodal function optimization.

/pdf/learning-and-optimization-using-the-clonal-selection-fjnwsvdguv.pdf

Learning and optimization using the clonal selection principle

Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete.

/pdf/evolutionary-computation-comments-on-the-history-and-current-2wv5xjjqp7.pdf

Evolutionary computation: comments on the history and current state

Robustness is an important requirement for almost all kinds of products. This article shows how evolutionary algorithms can be applied for robust design based on the approach of Taguchi. To achieve a better understanding of the consequences of this approach, we first present some analytical results gained from a toy problem. As a nontrivial industrial application we consider the design of multilayer optical coatings (MOCs) most frequently used for optical filters. An evolutionary algorithm based on a parallel diffusion model and extended for mixed-integer optimization was able to compete with or even outperform traditional methods of robust MOC design. With respect to chromaticity, the MOC designs found by the evolutionary algorithm are substantially more robust to parameter variations than a reference design and therefore perform much better in the average case. In most cases, however, this advantage has to be paid for by a reduction in the average reflectance. The robust design approach outlined in this paper should be easily adopted to other application domains.

Robust design of multilayer optical coatings by means of evolutionary algorithms

Evolution Strategies are reported to be robust in the presence of noise which in general hinders the optimization process. In this paper we discuss the influence of some of the stratey parameters and strategy variants on the convergence process and discuss measures for improvement of the convergence properties. After having a broad look to the theory for the dynamics of a (1,λ)-ES on a simple quadratic function we numerically investigate the influence of the parent population size and the introduction of recombination. Finally we compare the effects of multiple sampling of the objective function versus the enlargment of the population size for the convergence precision as well as the convergence reliability by the example of the multimodal Rastrigins function.

Evolution Strategies on Noisy Functions: How to Improve Convergence Properties

The structural development of human settlements can be characterized as a complex highly feedbacketed process. The assumption that this process is governed by rather few fundamental laws stimulated a considerable research in the field of urban growth during the last decade. Aiming at the comprehension of the basic underlying dynamics different approaches from the field of self-organizing systems have been proposed. In this paper we present a “cellular model” of urban growth dynamics based on the work of White, Engelen and Uljee. As a starting point this model throws some light on the mechanism of urban growth. But even more important, it raises a lot of questions concerning the requirements for a sound modeling technique. One of these questions is how to assess the complex patterns produced by single simulation runs. Here we make especially use of fractal measures reducing the contained information to a manageable size. No doubt, this field of research is still in its infant state, and we are far from being able to hand a reliable tool to the decision maker. But promising results will stimulate future investigations which we hope will lead to a deeper insight into the nature of cities to improve the living conditions of their inhabitants.

Modeling Urban Growth by Cellular Automata

Robustness is an important requirement for almost all kinds of products. In this article we show how evolutionary algorithms can be applied for robust design based on the approach of Taguchi. As an example we consider the design of multilayer optical coatings most frequently used for optical lters.

Ulrich Hammel

Papers

Evolutionary computation: comments on the history and current state

Robust design of multilayer optical coatings by means of evolutionary algorithms

Evolution Strategies on Noisy Functions: How to Improve Convergence Properties

Modeling Urban Growth by Cellular Automata

Robust Design of Multilayer Optical Coatings by Means of Evolution Strategies