An important aspect of a classifier system, especially in a control application, is how the state space is partitioned. Many applications take for granted a particular partitioning of the state space, while in others, the appropriate partitioning of the state space is itself part of the problem (Melhuish and Fogarty 1994).

对一个分类器系统，由其在控制应用中，状态空间是怎么分割的是很重要的问题。

Game playing is another application for which classification plays a key role. Although EC is often applied to rather simple games (e.g. the prisoner’s dilemma (Axelrod 1987, Fogel 1993b)), this is sometimes motivated by more serious applications, such as military ones (e.g. the two-tanks game (Fairley and Yates 1994) and air combat maneuvering.

玩游戏是另一个分类起重要作用的应用。

EC has been hybridized with feature partitioning and applied to a range of tasks (Gu ̈venir and S ̧irin 1993), including classification of iris flowers, prediction of survival for heart attack victims from echocardiogram data, diagnosis of heart disease, and classification of glass samples. In linguistics, EC has been applied to the classification of Swedish words.
In economics, Oliver (1993) has found rules to reflect the way in which consumers choose one brand rather than another, when there are multiple criteria on which to judge a product. A fuzzy hybrid system has been used for financial decision making, with applications to credit evaluation, risk assessment, and insurance underwriting.

经济学的顾客选择品牌问题。信用评估、风险评估和保险风险评估。

In biology, EC has been applied to the difficult task of protein secondary- structure determination, for example, classifying the locations of particular protein segments (Handley 1993). It has also been applied to the classification of soil samples (Punch et al 1993).

蛋白质二级结构的决定。

In image processing, there have been further military applications, classifying features in images as targets (Bala and Wechsler 1993, Tackett 1993), and also non-military applications, such as optical character recognition.
Of increasing importance is the efficient storage and retrieval of information, including the generation of equifrequency distributions of material, to improve that efficiency. EC has also been employed to assist with the representation and storage of chemical structures, and the retrieval from databases of molecules containing certain substructures (Jones et al 1993). The retrieval of documents which match certain characteristics is becoming increasingly important as more and more information is held on-line. Tools to retrieve documents which contain specified words have been available for many years, but they have the limitation that constructing an appropriate search query can be difficult. Researchers are now using EAs to help with query construction (Yang and Korfhage 1993).

有效率的存储和数据读取。表达和存储化学结构。建立数据query结构。

2.7 Summary

EC has been applied in a vast number of application areas. In some cases it has advantages over existing computerized techniques. More interestingly, perhaps, it is being applied to an increasing number of areas in which computers have not been used before. We can expect to see the number of applications grow considerably in the future. Comprehensive bibliographies in many different application areas are listed after the References.
有很多用处。

3 Advantages (and disadvantages) of evolutionary computation over other approaches

3.1 No-free-lunch theorem

Since, according to the no-free-lunch (NFL) theorem (Wolpert and Macready 1996), there cannot exist any algorithm for solving all (e.g. opti- mization) problems that is generally (on average) superior to any competitor, the question of whether evolutionary algorithms (EAs) are inferior/superior to any alternative approach is senseless. What could be claimed solely is that EAs behave better than other methods with respect to solving a specific class of problems—with the consequence that they behave worse for other problem classes.

EA有时好有时不如其他方法好。

The NFL theorem can be corroborated in the case of EAs versus many classical optimization methods insofar as the latter are more efficient in solving linear, quadratic, strongly convex, unimodal, separable, and many other special problems. On the other hand, EAs do not give up so early when discontinuous, nondifferentiable, multimodal, noisy, and otherwise unconventional response surfaces are involved. Their effectiveness (or robustness) thus extends to a broader field of applications, of course with a corresponding loss in efficiency when applied to the classes of simple problems classical procedures have been specifically devised for.

其他经典优化问题对解决线性、平方、严格凸等等特别问题好。EA尝试解决不连续、不可微分、多目标、噪音问题。能解决的问题更广。

Looking into the historical record of procedures devised to solve optimization problems, especially around the 1960s (see the book by Schwefel (1995)), when a couple of direct optimum-seeking algorithms were published, for example, in the Computer Journal, a certain pattern of development emerges. Author A publishes a procedure and demonstrates its suitability by means of tests using some test functions. Next, author B comes along with a counterexample showing weak performance of A’s algorithm in the case of a certain test problem. Of course, he also presents a new or modified technique that outperforms the older one(s) with respect to the additional test problem. This game could in principle be played ad infinitum.

20世纪90年代，A提出好的优化算法，B换了测试问题，提出反例，然后周而复始。

A better means of clarifying the scene ought to result from theory. This should clearly define the domain of applicability of each algorithm by presenting convergence proofs and efficiency results. Unfortunately, however, it is possible to prove abilities of algorithms only by simplifying them as well as the situations to which they are confronted. The huge remainder of questions must be answered by means of (always limited) test series, and even that cannot tell much about an actual real-world problem-solving situation with yet unanalyzed features, that is, the normal case in applications.

应该综合很多测试做评价，但是也不是实际应用中的评价。

Again unfortunately, there does not exist an agreed-upon test problem catalogue to evaluate old as well as new algorithms in a concise way. It is doubtful whether such a test bed will ever be agreed upon, but efforts in that direction would be worthwhile.

没有公认的评价方法。

3.2 Conclusions

Finally, what are the truths and consequences? First, there will always remain a dichotomy between efficiency and general applicability, between reliability and effort of problem-solving, especially optimum-seeking, algorithms. Any specific knowledge about the situation at hand may be used to specify an adequate specific solution algorithm, the optimal situation being that one knows the solution in advance. On the other hand, there cannot exist one method that solves all problems effectively as well as efficiently. These goals are contradictory.

第一，效率和广泛应用性永远矛盾。

If there is already a traditional method that solves a given problem, EAs should not be used. They cannot do it better or with less computational effort. In particular, they do not offer an escape from the curse of dimensionality—the often quadratic, cubic, or otherwise polynomial increase in instructions used as the number of decision variables is increased, arising, for example, from matrix manipulation.

如果已经有了传统方法，EA不应该再去应用了。不可能更好。算法复杂度高。

To develop a new solution method suitable for a problem at hand may be a nice challenge to a theoretician, who will afterwards get some merit for his effort, but from the application point of view the time for developing the new technique has to be added to the computer time invested. In that respect, a nonspecialized, robust procedure (and EAs belong to this class) may be, and often proves to be, worthwhile.

???

A warning should be given about a common practice—the linearization or other decomplexification of the situation in order to make a traditional method applicable. Even a guaranteed globally optimal solution for the simplified task may be a long way off and thus greatly inferior to an approximate solution to the real problem.

线性化和去复杂化使得传统方法可行。会使得结论和实际相去甚远。

The best one can say about EAs, therefore, is that they present a methodological framework that is easy to understand and handle, and is either usable as a black-box method or open to the incorporation of new or old recipes for further sophistication, specialization or hybridization. They are applicable even in dynamic situations where the goal or constraints are moving over time or changing, either exogenously or self-induced, where parameter adjustments and fitness measurements are disturbed, and where the landscape is rough, discontinuous, multimodal, even fractal or cannot otherwise be handled by traditional methods, especially those that need global prediction from local surface analysis.

EA最好的地方在于提供方法论框架，很好理解和处理，也可作为黑箱也可以开放合作。甚至可以用在动态情景。广泛性。

There exist EA versions for multiple criterion decision making (MCDM) and many different parallel computing architectures. Almost forgotten today is their applicability in experimental (non-computing) situations.
Sometimes striking is the fact that even obviously wrong parameter settings do not prevent fairly good results: this certainly can be described as robustness. Not yet well understood, but nevertheless very successful are those EAs which self-adapt some of their internal parameters, a feature that can be described as collective learning of the environmental conditions. Nevertheless, even self- adaptation does not circumvent the NFL theorem.

不好的参数设定也有好的结果，这是robust性。成功自适应。然而，自适应没有解决NFL问题。

In this sense, and only in this sense, EAs always present an intermediate compromise; the enthusiasm of their inventors is not yet taken into account here, nor the insights available from the analysis of the algorithms for natural evolutionary processes which they try to mimic.

就这个意义而言，EA是一种妥协。

4 Principles of evolutionary processes

4.1 Overview

The most widely accepted collection of evolutionary theories is the neo- Darwinian paradigm. These arguments assert that the vast majority of the history of life can be fully accounted for by physical processes operating on and within populations and species (Hoffman 1989, p 39). These processes are reproduction, mutation, competition, and selection. Reproduction is an obvious property of extant species. Further, species have such great reproductive potential that their population size would increase at an exponential rate if all individuals of the species were to reproduce successfully (Malthus 1826, Mayr 1982, p 479). Reproduction is accomplished through the transfer of an individual’s genetic program (either asexually or sexually) to progeny. Mutation, in a positively entropic system, is guaranteed, in that replication errors during information transfer will necessarily occur. Competition is a consequence of expanding populations in a finite resource space. Selection is the inevitable result of competitive replication as species fill the available space. Evolution becomes the inescapable result of interacting basic physical statistical processes (Huxley 1963, Wooldridge 1968, Atmar 1979).
Individuals and species can be viewed as a duality of their genetic program, the genotype (Section 5.2), and their expressed behavioral traits, the phenotype. The genotype provides a mechanism for the storage of experiential evidence, of historically acquired information. Unfortunately, the results of genetic variations are generally unpredictable due to the universal effects of pleiotropy and polygeny (figure 4.1) (Mayr 1959, 1963, 1982, 1988, Wright 1931, 1960, Simpson 1949, p 224, Dobzhansky 1970, Stanley 1975, Dawkins 1986). Pleiotropy is the effect that a single gene may simultaneously affect several phenotypic traits. Polygeny is the effect that a single phenotypic characteristic may be determined by the simultaneous interaction of many genes. There are no one-gene, one-trait relationships in naturally evolved systems. The phenotype varies as a complex, nonlinear function of the interaction between underlying genetic structures and current environmental conditions. Very different genetic structures may code for equivalent behaviors, just as diverse computer programs can generate similar functions.
Selection directly acts only on the expressed behaviors of individuals and species (Mayr 1988, pp 477–8). Wright (1932) offered the concept of adaptive topography to describe the fitness of individuals and species (minimally, isolated reproductive populations termed demes). A population of genotypes maps to respective phenotypes (sensu Lewontin 1974), which are in turn mapped onto the adaptive topography (figure 4.2). Each peak corresponds to an optimized collection of phenotypes, and thus to one of more sets of optimized genotypes. Evolution probabilistically proceeds up the slopes of the topography toward peaks as selection culls inappropriate phenotypic variants.

选择只作用于表现型，Wright提出了适应性地形图的概念用于描述个体和种群的适应性。山峰对应表现型，对应一个或多个优的基因型。

Others (Atmar 1979, Raven and Johnson 1986, pp 400–1) have suggested that it is more appropriate to view the adaptive landscape from an inverted position. The peaks become troughs, ‘minimized prediction error entropy wells’ (Atmar 1979). Searching for peaks depicts evolution as a slowly advancing, tedious, uncertain process. Moreover, there appears to be a certain fragility to an evolving phyletic line; an optimized population might be expected to quickly fall of the peak under slight perturbations. The inverted topography leaves an altogether different impression. Populations advance rapidly down the walls of the error troughs until their cohesive set of interrelated behaviors is optimized, at which point stagnation occurs. If the topography is generally static, rapid descents will be followed by long periods of stasis. If, however, the topography is in continual flux, stagnation may never set in.
Viewed in this manner, evolution is an obvious optimizing problem- solving process (not to be confused with a process that leads to perfection). Selection drives phenotypes as close to the optimum as possible, given initial conditions and environment constraints. However the environment is continually changing. Species lag behind, constantly evolving toward a new optimum. No organism should be viewed as being perfectly adapted to its environment. The suboptimality of behavior is to be expected in any dynamic environment that mandates tradeoffs between behavioral requirements. However selection never ceases to operate, regardless of the population’s position on the topography.
Mayr (1988, p 532) has summarized some of the more salient characteristics of the neo-Darwinian paradigm. These include:
(i) The individual is the primary target of selection.
(ii) Genetic variation is largely a chance phenomenon. Stochastic processes play a significant role in evolution.
(iii) Genotypic variation is largely a product of recombination and ‘only ultimately of mutation’.
(iv) ‘Gradual’ evolution may incorporate phenotypic discontinuities.
(v) Not all phenotypic changes are necessarily consequences of ad hoc natural selection.
(vi) Evolution is a change in adaptation and diversity, not merely a change in gene frequencies.
(vii) Selection is probabilistic, not deterministic.These characteristics form a framework for evolutionary computation.
个体是主要目标，基因变化主要是随机过程，基因变化是重组？？，逐渐进化可能会表现型不连续，不是所有的表现型变化都是特定自然选择的结果，进化是适应性和差异性的改变不仅仅是基因频率，选择是概率不是决定性的。这些特点组成了进化的框架。

5 Principles of genetics

5.1 Introduction

The material covers a number of key areas which are necessary to understanding the nature of the evolutionary process. We begin by looking at some basic ideas of heredity and how variation occurs in interbreeding populations. From here we look at the gene in more detail and then consider how it can undergo change. The next section looks at aspects of population thinking needed to appreciate selection. This is crucial to an appreciation of Darwinian mechanisms of evolution. The chapter concludes with selected references to further information. In order to keep this contribution within its size limits, the material is primarily about the biology of higher plants and animals.

5.2 Some fundamental concepts in genetics

Many plants and animals are produced through sexual means by which the nucleus of a male sperm cell fuses with a female egg cell (ovum). Sperm and ovum nuclei each contain a single complement of nuclear material arranged as ribbon-like structures called chromosomes. When a sperm fuses with an egg the resulting cell, called a zygote, has a double complement of chromosomes together with the cytoplasm of the ovum. We say that a single complement of chromosomes constitutes a haploid set (abbreviated as n) and a double complement is called the diploid set (2n). Gametes (sex cells) are haploid whereas most other cells are diploid. The formation of gametes (gametogenesis) requires the number of chromosomes in the gamete-forming cells to be halved (see figure 5.1).

6 A history of evolutionary computation

6.1 Introduction

No one will ever produce a completely accurate account of a set of past events since, as someone once pointed out, writing history is as difficult as forecasting. Thus we dare to begin our historical summary of evolutionary computation rather arbitrarily at a stage as recent as the mid-1950s.
At that time there was already evidence of the use of digital computer models to better understand the natural process of evolution. One of the first descriptions of the use of an evolutionary process for computer problem solving appeared in the articles by Friedberg (1958) and Friedberg et al (1959). This represented some of the early work in machine learning and described the use of an evolutionary algorithm for automatic programming, i.e. the task of finding a program that calculates a given input–output function. Other founders in the field remember a paper of Fraser (1957) that influenced their early work, and there may be many more such forerunners depending on whom one asks.