Showing papers on "Evolutionary programming published in 1995"

A Survey of Constraint Handling Techniques in Evolutionary Computation Methods.

Abstract: One of the major components of any evolutionary system is the evaluation function. Evaluation functions are used to assign a quality measure for individuals in a population. Whereas evolutionary computation techniques assume the existence of an (e cient) evaluation function for feasible individuals, there is no uniform methodology for handling (i.e., evaluating) unfeasible ones. The simplest approach, incorporated by evolution strategies and a version of evolutionary programming (for numerical optimization problems), is to reject unfeasible solutions. But several other methods for handling unfeasible individuals have emerged recently. This paper reviews such methods (using a domain of nonlinear programming problems) and discusses their merits and drawbacks.

Power system optimal reactive power dispatch using evolutionary programming

Abstract: This paper is concerned with application of evolutionary programming (EP) to optimal reactive power dispatch and voltage control of power systems. Practical implementation of the EP for global optimization problems of large-scale power systems has been considered. The proposed EP method has been evaluated on the IEEE 30-bus system. Simulation results, compared with those obtained using a conventional gradient-based optimization method, are presented to show the potential of application of the proposed method to power system economical operations. >

Contemporary Evolution Strategies

Abstract: After an outline of the history of evolutionary algorithms, a new (μ, κ, λ, ρ) variant of the evolution strategies is introduced formally. Though not comprising all degrees of freedom, it is richer in the number of features than the meanwhile old (μ, λ) and (μ+λ) versions. Finally, all important theoretically proven facts about evolution strategies are briefly summarized and some of many open questions concerning evolutionary algorithms in general are pointed out.

Adapting Crossover in Evolutionary Algorithms.

Abstract: One of the issues in evolutionary algorithms (EAs) is the relative importance of two search operators: mutation and crossover. Genetic algorithms (GAs) and genetic programming (GP) stress the role of crossover, while evolutionary programming (EP) and evolution strategies (ESs) stress the role of mutation. The existence of many different forms of crossover further complicates the issue. Despite theoretical analysis, it appears difficult to decide a priori which form of crossover to use, or even if crossover should be used at all. One possible solution to this difficulty is to have the EA be self-adaptive, i.e., to have the EA dynamically modify which forms of crossover to use and how often to use them, as it solves a problem. This paper describes an adaptive mechanism for controlling the use of crossover in an EA and explores the behavior of this mechanism in a number of different situations. An improvement to the adaptive mechanism is then presented. Surprisingly this improvement can also be used to enhance performance in a non-adaptive EA.

Complexity Compression and Evolution

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Identification of ARMAX model for short term load forecasting: an evolutionary programming approach

Abstract: This paper proposes a new evolutionary programming (EP) approach to identify the autoregressive moving average with exogenous variable (ARMAX) model for one day to one week ahead hourly load demand forecasts. Typically, the surface of forecasting error function possesses multiple local minimum points. Solutions of the traditional gradient search based identification technique therefore may stall at the local optimal points which lead to an inadequate model. By simulating natural evolutionary process, the EP algorithm offers the capability of converging towards the global extremum of a complex error surface. The developed EP based load forecasting algorithm is verified by using different types of data for practical Taiwan power (Taipower) system and substation load as well as temperature values. Numerical results indicate the proposed EP approach provides a method to simultaneously estimate the appropriate order and parameter values of the ARMAX model for diverse types of load data. Comparisons of forecasting errors are made to the traditional identification techniques.

Building Better Test Functions

Abstract: We introduce basic guidelines for developing test suites for evolutionary algorithms and examine common test functions in terms of these guidelines. Two methods of designing test functions are introduced which address speciic issues relevant to comparative studies of evolutionary algorithms. The rst method produces representation invariant functions. The second method constructs functions with diierent degrees of nonlinearity, where the interactions and the cost of evaluation scale with respect to the dimensionality of the search space.

Evolving Globally Synchronized Cellular Automata

Abstract: How does an evolutionary process interact with a decentralized distributed system in order to produce globally coordinated behavior Using a genetic algorithm GA to evolve cellular au tomata CAs we show that the evolution of spontaneous synchronization one type of emergent coordination takes advantage of the underlying medium s potential to form embedded particles The particles typically phase defects between synchronous regions are designed by the evolu tionary process to resolve frustrations in the global phase We describe in detail one typical solution discovered by the GA delineating the discovered synchronization algorithm in terms of embedded particles and their interactions We also use the particle level description to analyze the evolutionary sequence by which this solution was discovered Our results have implications both for understanding emergent collective behavior in natural systems and for the automatic programming of decentralized spatially extended multiprocessor systems

Fitness Variance of Formae and Performance Prediction

Abstract: Representation is widely recognised as a key determinant of performance in evolutionary computation. The development of families of representation-independent operators allows the formulation of formal representation-independent evolutionary algorithms. These formal algorithms can be instantiated for particular search problems by selecting a suitable representation. The performance of different representations, in the context of any given formal representation-independent algorithm, can then be measured. Simple analyses suggest that fitness variance of formae (generalised schemata) for the chosen representation might act as a performance predictor for evolutionary algorithms. This hypothesis is tested and supported through studies of four different representations for the travelling sales-rep problem (TSP) in the context of both formal representation-independent genetic algorithms and corresponding memetic algorithms.

Alternative neural network training methods [active sonar processing]

Abstract: Investigates three potential neural network training algorithms in processing active sonar returns. Although all three methods generate reasonable probabilities of detection and false alarm in discriminating between man-made objects and background events, the stochastic training methods of simulated annealing and evolutionary programming outperform backpropagation. >

Evolving neural control systems

Abstract: Controlling unstable nonlinear systems with neural networks can be problematic. Two examples show that evolutionary programming provides a feasible method for addressing such control problems. >

A Comparison of Evolutionary Programming and Genetic Algorithms on Selected Constrained Optimization Problems

Abstract: Evolutionary programming and genetic algorithms are compared on two constrained optimization problems. The constrained problems are redesigned as related unconstrained problems by the application o...

A comparison of methods for self-adaptation in evolutionary algorithms

Abstract: Evolutionary algorithms, including evolutionary programming and evolution strategies, have often been applied to real-valued function optimization problems. These algorithms generally operate directly on the real values to be optimized, in contrast with genetic algorithms which usually operate on a separately coded transformation of the objective variables. Evolutionary algorithms often rely on a second-level optimization of strategy parameters, tunable variables that in part determine how each parent will generate offspring. Two alternative methods for performing this second-level optimization have been proposed and are compared across a series of function optimization tasks. The results appear to favor the approach offered originally in evolution strategies, although the applicability of the findings may be limited to the case where each parameter of a parent solution is perturbed independently of all others.

Evolutionary identification of cloth animation models

Abstract: This paper presents an application of evolutionary genetic techniques to the identification of internal parameters of a mass-spring physically-based animation model.

An Evolutionary Programming Approach To Self-Adaptation On Finite State Machines

Abstract: Evolutionary programming was first offered as an alternative method for generating artificial intelligence. Experiments were offered in which finite state machines were used to predict time series with respect to an arbitrary payoff function. Mutations were imposed on the evolving machines such that each of the possible modes of variation were given equal probability. The current study investigates the use of self-adaptive methods of evolutionary programming on finite state machines. Each machine incorporates a coding for its structure and an additional set of parameters that determine in part how it will distribute new trials. Two methods for accomplishing this self-adaptation are implemented and tested on two simple prediction problems. The results appear to favor the use of such self-adaptive methods.

Hierarchical Evolutionary Theory: There Is an Alternative, and It's Not Creationism

Abstract: (1995). Hierarchical Evolutionary Theory: There Is an Alternative, and It's Not Creationism. Psychological Inquiry: Vol. 6, No. 1, pp. 31-34.

Phenotypes, genotypes, and operators in evolutionary computation

Abstract: Evolutionary computation can be conducted at various levels of abstraction (e.g., genes, individuals, species). Recent claims have been made that simulated evolution can be made more biologically accurate by applying specific genetic operators that mimic low-level transformations to DNA. This paper argues instead that the appropriateness of particular variation operators depends on the level of abstraction of the simulation. Further, including spec@ random variation operators simply because they have a similar form as genetic operators that occur in nature does not, in general, lead lo greater fdelity in simulation.

Continuous evolutionary programming: analysis and experiments

Abstract: Evolutionary programming is a method for simulating evolution that emphasizes the behavioral rather than the genetic relationship of parents and their offspring. In a typical evolutionary program, every parent simultaneously generates a number of offspring, which are all subsequently placed in competition. Evolution can be abstracted as a more continuous process by generating only a single offspring from one parent and then immediately placing it in competition with all existing solutions. Some theoretical observations are made with respect to this new model. The results of empirical trials on a test landscape with multiple local minima indicate that the standard method of reproduction and selection may be more appropriate for practical optimization problems.

An Introduction to Evolutionary Programming

Abstract: Evolutionary programming is a method for simulating evolution that has been investigated for over 30 years. This paper offers an introduction to evolutionary programming, and indicates its relationship to other methods of evolutionary computation, specifically genetic algorithms and evolution strategies. The original efforts that evolved finite state machines for predicting arbitrary time series, as well as specific recent efforts in combinatorial and continuous optimization are reviewed. Some areas of current investigation are mentioned, including empirical assessment of the optimization performance of the technique and extensions of the method to include mechanisms to self-adapt to the error surface being searched.

Evolution Strategies: An Alternative Evolutionary Algorithm

Abstract: In this paper, evolution strategies (ESs) — a class of evolutionary algorithms using normally distributed mutations, recombination, deterministic selection of the μ>1 best offspring individuals, and the principle of self-adaptation for the collective on-line learning of strategy parameters — are described by demonstrating their differences to genetic algorithms. By comparison of the algorithms, it is argued that the application of canonical genetic algorithms for continuous parameter optimization problems implies some difficulties caused by the encoding of continuous object variables by binary strings and the constant mutation rate used in genetic algorithms. Because they utilize a problem-adequate representation and a suitable self-adaptive step size control guaranteeing linear convergence for strictly convex problems, evolution strategies are argued to be more adequate for continuous problems.

A Survey of Constraint Handling Techniques in Evolutionary Computation Methods

Abstract: One of the major components of any evolutionary system is the evaluation function. Evaluation functions are used to assign a quality measure for individuals in a population. Whereas evolutionary computation techniques assume the existence of an (efficient) evaluation function for feasible individuals, there is no uniform methodology for handling (i.e., evaluating) unfeasible ones. The simplest approach, incorporated by evolution strategies and a version of evolutionary programming (for numerical optimization problems), is to reject unfeasible solutions. But several other methods for handling unfeasible individuals have emerged recently. This paper reviews such methods (using a domain of nonlinear programming problems) and discusses their merits and drawbacks.

Preliminary Investigations Into A Two-Stage Method of Evolutionary Optimization On Constrained Problems

Abstract: A hybrid of evolutionary programming and a deterministic optimization procedure is applied to a series of nonlinear and quadratic optimization problems. The results indicate that the hybrid method can outperform the exclusive use of evolutionary programming when addressing constrained optimization problems with finite penalty functions. Directions for future research are outlined.

Hybrid evolutionary method for obstacle location-allocation

Abstract: In this paper we propose a hybrid evolutionary method for Obstacle Location-allocation problem This problem can be described as a tri-level mixed integer programming problem Since this problem is very complex and with many local solutions, no direct method Is effective to solve it, Heuristic methods were proposed to it, but optimality is not guaranteed yet Our hybrid evolutionary method adopts the main structure of Genetic Algorithms (GA) absorbing ideas from Evolutionary Strategy (ES) and combines with some traditional optimization techniques In this way we can pursue global optimization maintaining a good efficiency of our method A case study shows the effectiveness of this method

The Phase-Transition Niche for Evolutionary Algorithms in Timetabling

Abstract: Constraint satisfaction problems tend to display phase transitions with respect to the effort required by specific problem solving strategies. So far, little is known concerning the causes of phase transitions, or the relative differences between performance of different algorithms around them, especially with respect to stochastic iterative methods such as evolutionary search. Also, work so far on phase transitions concentrates on homogeneous random problems, rather than problems displaying elements of structure typical of more realistic problems. We investigate some of these issues, and uncover some new phase transition regions on timetabling style problems, occurring in the context of varying degrees of problem homogenity as well as (the more standard) graph connectivity. Further, we find that a simple evolutionary algorithm outperforms a simple Stochastic Hillclimber in regions strongly associated with certain phase transitions, and not others. Finally, we discuss various clues to the underlying causes of these phase transitions.