Showing papers on "Evolutionary programming published in 1997"

Handbook of Evolutionary Computation

Abstract: From the Publisher: Many scientists and engineers now use the paradigms of evolutionary computation (genetic agorithms, evolution strategies, evolutionary programming, genetic programming, classifier systems, and combinations or hybrids thereof) to tackle problems that are either intractable or unrealistically time consuming to solve through traditional computational strategies Recently there have been vigorous initiatives to promote cross-fertilization between the EC paradigms, and also to combine these paradigms with other approaches such as neural networks to create hybrid systems with enhanced capabilities To address the need for speedy dissemination of new ideas in these fields, and also to assist in cross-disciplinary communications and understanding, Oxford University Press and the Institute of Physics have joined forces to create a major reference publication devoted to EC fundamentals, models, algorithms and applications This work is intended to become the standard reference resource for the evolutionary computation community The Handbook of Evolutionary Computation will be available in loose-leaf print form, as well as in an electronic version that combines both CD-ROM and on-line (World Wide Web) acess to its contents Regularly published supplements will be available on a subscription basis

Evolutionary computation: comments on the history and current state

Abstract: 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.

A new evolutionary system for evolving artificial neural networks

Abstract: This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP). Unlike most previous studies on evolving ANN's, this paper puts its emphasis on evolving ANN's behaviors. Five mutation operators proposed in EPNet reflect such an emphasis on evolving behaviors. Close behavioral links between parents and their offspring are maintained by various mutations, such as partial training and node splitting. EPNet evolves ANN's architectures and connection weights (including biases) simultaneously in order to reduce the noise in fitness evaluation. The parsimony of evolved ANN's is encouraged by preferring node/connection deletion to addition. EPNet has been tested on a number of benchmark problems in machine learning and ANNs, such as the parity problem, the medical diagnosis problems, the Australian credit card assessment problem, and the Mackey-Glass time series prediction problem. The experimental results show that EPNet can produce very compact ANNs with good generalization ability in comparison with other algorithms.

Fast Evolution Strategies

Abstract: Evolution strategies are a class of general optimisation algorithms which are applicable to functions that are multimodal, non-differentiable, or even discontinuous. Although recombination operators have been introduced into evolution strategies, their primary search operator is still mutation. Classical evolution strategies rely on Gaussian mutations. A new mutation operator based on the Cauchy distribution is proposed in this paper. It is shown empirically that the new evolution strategy based on Cauchy mutation outperforms the classical evolution strategy on most of the 23 benchmark problems tested in this paper. These results, along with those obtained by fast evolutionary programming

Evolutionary Algorithms in Engineering Applications

Abstract: Evolutionary algorithms are general-purpose search procedures based on the mechanisms of natural selection and population genetics. They are appealing because they are simple, easy to interface, and easy to extend. This volume is concerned with applications of evolutionary algorithms and associated strategies in engineering. It will be useful for engineers, designers, developers, and researchers in any scientific discipline interested in the applications of evolutionary algorithms. The volume consists of five parts, each with four or five chapters. The topics are chosen to emphasize application areas in different fields of engineering. Each chapter can be used for self-study or as a reference by practitioners to help them apply evolutionary algorithms to problems in their engineering domains.

Adaptation in evolutionary computation: a survey

Abstract: Adaptation of parameters and operators is one of the most important and promising areas of research in evolutionary computation; it tunes the algorithm to the problem while solving the problem. In this paper we develop a classification of adaptation on the basis of the mechanisms used, and the level at which adaptation operates within the evolutionary algorithm. The classification covers all forms of adaptation in evolutionary computation and suggests further research.

Application of evolutionary programming to reactive power planning-comparison with nonlinear programming approach

Abstract: This paper proposes an application of evolutionary programming (EP) to reactive power planning (RPP). Several techniques have been developed to make EP practicable to solve a real power system problem and other practical problems. The proposed approach has been used in the IEEE 30-bus system and a practical power system. For illustration purposes, only results for the IEEE 30-bus system are given. Simulation results, compared with those obtained by using a conventional gradient-based optimization method, Broyden's method, are presented to show that the present method is better for power system planning. In the case of optimization of noncontinuous and nonsmooth functions, EP is much better than nonlinear programming. The comprehensive simulation results show a great potential for applications of EP in power system economical and secure operation, planning and reliability assessment.

Evolutionary programming techniques for constrained optimization problems

Abstract: Two evolutionary programming (EP) methods are proposed for handling nonlinear constrained optimization problems. The first, a hybrid EP, is useful when addressing heavily constrained optimization problems both in terms of computational efficiency and solution accuracy. But this method offers an exact solution only if both the mathematical form of the objective function to be minimized/maximized and its gradient are known. The second method, a two-phase EP (TPEP) removes these restrictions. The first phase uses the standard EP, while an EP formulation of the augmented Lagrangian method is employed in the second phase. Through the use of Lagrange multipliers and by gradually placing emphasis on violated constraints in the objective function whenever the best solution does not fulfill the constraints, the trial solutions are driven to the optimal point where all constraints are satisfied. Simulations indicate that the TPEP achieves an exact global solution without gradient information, with less computation time than the other optimization methods studied here, for general constrained optimization problems.

Developing a new transformer fault diagnosis system through evolutionary fuzzy logic

Abstract: To improve the diagnosis accuracy of the conventional dissolved gas analysis (DGA) approaches, this paper proposes an evolutionary programming (EP) based fuzzy system development technique to identify the incipient faults of the power transformers. Using the IEC/IEEE DGA criteria as references, a preliminary framework of the fuzzy diagnosis system is first built. Based on previous dissolved gas test records and their actual fault types, the proposed EP-based development technique is then employed to automatically modify the fuzzy if-then rules and simultaneously adjust the corresponding membership functions. In comparison to results of the conventional DGA and the artificial neural networks (ANN) classification methods, the proposed method has been verified to possess superior performance both in developing the diagnosis system and in identifying the practical transformer fault cases.

Tracking Extrema in Dynamic Environments

Abstract: Typical applications of evolutionary optimization involve the off-line approximation of extrema of static multi-modal functions. Methods which use a variety of techniques to self-adapt mutation parameters have been shown to be more successful than methods which do not use self-adaptation. For dynamic functions, the interest is not to obtain the extrema but to follow it as closely as possible. This paper compares the on-line extrema tracking performance of an evolutionary program without self-adaptation against an evolutionary program using a self-adaptive Gaussian update rule over a number of dynamics applied to a simple static function. The experiments demonstrate that for some dynamic functions, self-adaptation is effective while for others it is detrimental.

Evolutionary consequences of coevolving targets

Abstract: Most evolutionary optimization models incorporate a fitness evaluation that is based on a predefined static set of test cases or problems. In the natural evolutionary process, selection is of course not based on a static fitness evaluation. Organisms do not have to combat every existing disease during their lifespan; organisms of one species may live in different or changing environments; different species coevolve. This leads to the question of how information is integrated over many generations. This study focuses on the effects of different fitness evaluation schemes on the types of genotypes and phenotypes that evolve. The evolutionary target is a simple numerical function. The genetic representation is in the form of a program (i.e., a functional representation, as in genetic programming). Many different programs can code for the same numerical function. In other words, there is a many-to-one mapping between “genotypes” (the programs) and “phenotypes”. We compare fitness evaluation based on a large static set of problems and fitness evaluation based on small coevolving sets of problems. In the latter model very little information is presented to the evolving programs regarding the evolutionary target per evolutionary time step. In other words, the fitness evaluation is very sparse. Nevertheless the model produces correct solutions to the complete evolutionary target in about half of the simulations. The complete evaluation model, on the other hand, does not find correct solutions to the target in any of the simulations. More important, we find that sparse evaluated programs are better generalizable compared to the complete evaluated programs when they are evaluated on a much denser set of problems. In addition, the two evaluation schemes lead to programs that differ with respect to mutational stability; sparse evaluated programs are less stable than complete evaluated programs.

Evolving computer programs without subtree crossover

Abstract: An evolutionary programming procedure is used for optimizing computer programs in the form of symbolic expressions. Six tree mutation operators are proposed. Recombination operators such as crossover are not included. The viability and efficiency of the method is extensively investigated on a set of well-studied problems. The evidence indicates that the technique is not only viable but is indeed capable of evolving good computer programs. The results compare well with other evolutionary methods that rely on crossover to solve the same problems.

Computer Evolution of Buildable Objects

Abstract: Creating artificial life forms through evolutionary robotics faces a “chicken and egg” problem: learning to control a complex body is dominated by inductive biases specific to its sensors and effectors, while building a body which is controllable is conditioned on the pre-existence of a brain. The idea of co-evolution of bodies and brains is becoming popular, but little work has been done in evolution of physical structure because of the lack of a general framework for doing it. Evolution of creatures in simulation has been constrained by the “reality gap” which implies that resultant objects are usually not buildable. The work we present takes a step in the problem of body evolution by applying evolutionary techniques to the design of structures assembled out of parts. Evolution takes place in a simulator we designed, which computes forces and stresses and predicts failure for 2-dimensional Lego structures. The final printout of our program is a schematic assembly, which can then be built physically. We demonstrate its functionality in several different evolved entities.

Distribution network reconfiguration for loss reduction using fuzzy controlled evolutionary programming

Abstract: Network reconfiguration for loss reduction in distribution systems is a very important way to save energy. However, due to its nature it is an inherently difficult optimisation problem. A new type of evolutionary search technique, evolutionary programming (EP), has been adopted and improved for this particular application. To improve the performance of EP, a fuzzy controlled EP (FCEP), based on heuristic information, is first proposed. The mutation fuzzy controller adaptively adjusts the mutation rate during the simulated evolutionary process. The status of each switch in distribution systems is naturally represented by a binary control parameter 0 or 1. The length of string is much shorter than those proposed by others. A chain-table and combined depth-first and breadth-first search strategy is employed to further speed up the optimisation process. The equality and inequality constraints are imbedded into the fitness function by penalty factors which guarantee the optimal solutions searched by the FCEP are feasible. The implementation of the proposed FCEP for feeder reconfiguration is described in detail. Numerical results are presented to illustrate the feasibility of the proposed FCEP.

The Advantages of Evolutionary Computation

Abstract: Evolutionary computation is becoming common in the solution of difficult, realworld problems in industry, medicine, and defense. This paper reviews some of the practical advantages to using evolutionary algorithms as compared with classic methods of optimization or artificial intelligence. Specific advantages include the flexibility of the procedures, as well as the ability to self-adapt the search for optimum solutions on the fly. As desktop computers increase in speed, the application of evolutionary algorithms will become routine.

Automatic generation of neural network architecture using evolutionary computation

Abstract: This paper reports the application of evolutionary computation in the automatic generation of a neural network architecture. It is a usual practice to use trial and error to find a suitable neural network architecture. This is not only time consuming but may not generate an optimal solution for a given problem. The use of evolutionary computation is a step towards automation in architecture generation. In this paper a brief introduction to the field is given as well as an implementation of automatic neural network generation using genetic programming. >

Using co-evolutionary programming to simulate strategic behaviour in markets

Abstract: This paper describes the use of a genetic algorithm (GA) to model several standard industrial organisation games: Bertrand and Cournot competition, a vertical chain of monopolies, and a simple model of an electricity pool. The intention is to demonstrate that the GA performs well as a modelling tool in these standard settings, and that evolutionary programming therefore has a potential role in applied work requiring detailed market simulation. The advantages of using a GA over scenario analysis for applied market simulation are outlined. Also explored are the way in which the equilibria discovered by the GA can be interpreted, and what the market analogue for the GA process might be.

Evolutionary programming

Abstract: This paper presents a mixed-variable evolutionary programming (MVEP) for solving mechanical design optimization problems which contain integer, discrete, zero-one and continuous variables. The MVEP provides an improvement in global search and convergence performance in a mixed-variable space. An approach to handle various kinds of variables and constraints is discussed. Two examples of mechanical design optimization are tested, which demonstrate that the proposed approach is superior to current methods for finding optimum solution, both in the quality of solution and convergence performance.

Basic Evolutionary Structural Optimization

Abstract: By slowly removing inefficient material from a structure, the shape of the structure evolves towards an optimum. This is the simple concept of evolutionary structural optimization (ESO). Various design constraints such as stiffness, frequency and buckling load may be imposed upon a structure. Depending on the types of design constraints, different rejection criteria for removing material need to be used. For each type of constraints, the corresponding rejection criteria will be discussed in detail in the subsequent chapters. This chapter describes the simplest rejection criterion based on local stress level. Several examples are included to illustrate how the ESO process works.

Theory of evolutionary algorithms and application to system synthesis

Abstract: 5 1 Overview of the Thesis 7 2 Global Optimization 11 2.1 The Global Optimization Problem . . . . . . . . . . . 12 2.2 Global Optimization Methods . . . . . . . . . . . . . . 14 2.3 Selected Optimization Methods . . . . . . . . . . . . . 17 2.3.1 Monte Carlo . . . . . . . . . . . . . . . . . . . 18 2.3.2 Hill Climbing . . . . . . . . . . . . . . . . . . . 19 2.3.3 Simulated Annealing . . . . . . . . . . . . . . . 20 2.3.4 Evolutionary Algorithms . . . . . . . . . . . . . 21 2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 23

Evolving artificial neural networks to combine financial forecasts

Abstract: We conduct evolutionary programming experiments to evolve artificial neural networks for forecast combination. Using stock price volatility forecast data we find evolved networks compare favorably with a naive average combination, a least squares method, and a kernel method on out-of-sample forecasting ability-the best evolved network showed strong superiority in statistical tests of encompassing. Further, we find that the result is not sensitive to the nature of the randomness inherent in the evolutionary optimization process.

An Analysis of Local Selection Algorithms in a Spatially Structured Evolutionary Algorithm.

Abstract: A critical part of the design of eeective EAs is to obtain a proper balance between exploration and exploitation. A key element of this balance is the selection algorithm used. Although we have for some time now understood the relative diierences between various selection methods for standard centralized EAs, our understanding of decentralized EAs has been less complete. In this paper we present analysis tools and results which provide considerable insight into the properties of various decentralized selection methods, including the selection pressures they induce. Understanding this permits a more informed choice of selection methods when designing and implementing spatially structured EAs.

An exploration into evolutionary models for non-routine design

Abstract: Non-routine design is characterized by the lack of knowledge regarding the relationships between the given requirements and the form required to satisfy those requirements. Thus a knowledge-lean methodology, as characterized by evolutionary models, seems suitable for non-routine design. However, evolutionary methods to date, as characterized by genetic algorithms (GAs), have been developed mainly for optimization and machine learning. This paper explores some of the issues involved in the use of evolutionary models as computational models in non-routine design.

Knowledge-based approaches to self-adaptation in cultural algorithms

Abstract: Cultural Algorithms are computational self-adaptive models which consist of a population and a belief space. Problem solving experience of individuals selected from the population space by the acceptance function is generalized and stored in the belief space. This knowledge can then control the evolution of the population component by means of the influence function. Here, we examine the role that different forms of knowledge can play in the self-adaptation process for evolution-based function optimizers. In particular, we compare various approaches using normative and situational knowledge in guiding the search process. Also we investigate the impact of different acceptance and influence functions on the system's performance by employing both static and flexible fuzzy approaches. Evolutionary Programming is used to implement the population space. The best performance is produced using knowledge to decide both step size and direction in most cases. In addition, the use of a fuzzy acceptance and influence function appears to be a promising one. All the results in this study exhibit that Cultural Algorithms are a naturally useful framework for self-adaptation and that the use of a cultural framework to support self-adaptation in Evolutionary Programming can produce substantial performance improvements as expressed in terms of (1) system success ratio, (2) execution CPU time, and (3) convergence (mean best solution) for a given set of function minimization problems. The nature of these improvements and the type of knowledge that is most effective in producing them depends on the structure of the problem. While in most cases, the best performance is produced using knowledge to decide both step size and direction, there are situations where controlling only the direction or the step size produces the best results. Also normative knowledge appears to be the dominant and general purpose knowledge source for the optimization functions here.