Showing papers on "Genetic algorithm published in 1995"

On genetic algorithms

Abstract: We analyze the performance of a Genetic Type Algorithm we call Culling and a variety of other algorithms on a problem we refer to as ASP. Culling is near optimal for this problem, highly noise tolerant, and the best known a~~roach . . in some regimes. We show that the problem of learning the Ising perception is reducible to noisy ASP. These results provide an example of a rigorous analysis of GA’s and give insight into when and how C,A’s can beat competing methods. To analyze the genetic algorithm, we view it as a special type of submartingale. We prove some new large deviation bounds on this submartingale w~ich enable us to determine the running time of the algorithm.

Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization.

Abstract: : This thesis incorporates a mixed discrete/continuous parameter genetic algorithm optimization capability into the Design Optimization/Markov Evaluation (DOME) program developed by the Charles Stark Draper Laboratory of Cambridge, Massachusetts. DOME combines the merits of Markov modeling and the Optimal Design Process to generate a systematic framework for system design with realistic reliability and cost analyses. The addition of genetic algorithms expands the design problem domain to include discrete parameter problems, which current optimization methods continue to struggle with. A new variant of the genetic algorithm called the steady-state genetic algorithm is introduced to eliminate the idea of distinct generations. Functional constraints are dealt with by ingenious use of the function information contained in the genetic algorithm population. The optimal genetic algorithm parameter settings are investigated, and the genetic algorithm is compared to the Monte Carlo method and the Branch and Bound method to show its relative utility in optimization. This research shows that a single criterion genetic algorithm can be expected to outperform other methods in efficiency, accuracy, and speed on problems of moderate to high complexity. The work then extends to multicriteria optimization, as applied to fault tolerant system design. A multicriteria genetic algorithm is created as a competitive means of generating the efficient (Pareto) set. Method parameters such as cloning, sharing, domination pressure, and population variability are investigated. The method is compared to the epsilon-constraint multi criteria method with a steady-state genetic algorithm performing the underlying single-criterion optimization.

An introduction to genetic algorithms for electromagnetics

Abstract: This article is a tutorial on using genetic algorithms to optimize antenna and scattering patterns. Genetic algorithms are "global" numerical-optimization methods, patterned after the natural processes of genetic recombination and evolution. The algorithms encode each parameter into binary sequences, called a gene, and a set of genes is a chromosome. These chromosomes undergo natural selection, mating, and mutation, to arrive at the final optimal solution. After providing a detailed explanation of how a genetic algorithm works, and a listing of a MATLAB code, the article presents three examples. These examples demonstrate how to optimize antenna patterns and backscattering radar-cross-section patterns. Finally, additional details about algorithm design are given. >

Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms

Abstract: A measure of search difficulty, fitness distance correlation (FDC), is introduced and its power as a predictor of genetic algorithm (GA) performance is investigated. The sign and magnitude of this correlation can be used to predict the performance of a GA on many problems where the global maxima are already known. FDC can be used to correctly classify easy deceptive problems and easy and difficult non-deceptive problems as difficult, it can be used to indicate when Gray coding will prove better than binary coding, it produces the expected answers when applied to problems over a wide range of apparent difficulty, and it is also consistent with the surprises encountered when GAs were used on the Tanese and royal road functions. The FDC measure is a consequence of an investigation into the connection between GAs and heuristic search.

Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms

Abstract: This paper examines the applicability of genetic algorithms (GA's) in the simultaneous design of membership functions and rule sets for fuzzy logic controllers. Previous work using genetic algorithms has focused on the development of rule sets or high performance membership functions; however, the interdependence between these two components suggests a simultaneous design procedure would be a more appropriate methodology. When GA's have been used to develop both, it has been done serially, e.g., design the membership functions and then use them in the design of the rule set. This, however, means that the membership functions were optimized for the initial rule set and not the rule set designed subsequently. GA's are fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. This new method has been applied to two problems, a cart controller and a truck controller. Beyond the development of these controllers, we also examine the design of a robust controller for the cart problem and its ability to overcome faulty rules. >

Removing the Genetics from the Standard Genetic Algorithm

Abstract: We present an abstraction of the genetic algorithm (GA), termed population-based incremental learning (PBIL), that explicitly maintains the statistics contained in a GA''s population, but which abstracts away the crossover operator and redefines the role of the population. This results in PBIL being simpler, both computationally and theoretically, than the GA. Empirical results reported elsewhere show that PBIL is faster and more effective than the GA on a large set of commonly used benchmark problems. Here we present results on a problem custom designed to benefit both from the GA''s crossover operator and from its use of a population. The results show that PBIL performs as well as, or better than, GAs carefully tuned to do well on this problem. This suggests that even on problems custom designed for GAs, much of the power of the GA may derive from the statistics maintained implicitly in its population, and not from the population itself nor from the crossover operator.

Large-scale economic dispatch by genetic algorithm

Abstract: This paper presents a new genetic approach for solving the economic dispatch problem in large-scale power systems. A new encoding technique is developed. The chromosome contains only an encoding of the normalized system incremental cost in this encoding technique. Therefore, the total number of bits of chromosome is entirely independent of the number of units. The salient feature makes the proposed genetic approach attractive in large and complex systems which other methodologies may fail to achieve. Moreover, the approach can take network losses, ramp rate limits, and prohibited zone avoidance into account because of genetic algorithm's flexibility. Numerical results on an actual utility system of up to 40 units show that the proposed approach is faster and more robust than the well-known lambda-iteration method in large-scale systems.

Evolutionary Algorithms, Fitness Landscapes and Search

Abstract: A new model of fitness landscapes suitable for the consideration of evolutionary and other search algorithms is developed and its consequences are investigated. Answers to the questions "What is a landscape?" "Are landscapes useful?" and "What makes a landscape difficult to search?" are provided. The model makes it possible to construct landscapes for algorithms that employ multiple operators, including operators that act on or produce multiple individuals. It also incorporates operator transition probabilities. The consequences of adopting the model include a "one operator, one landscape" view of algorithms that search with multiple operators. An investigation into crossover landscapes and hillclimbing algorithms on them illustrates the dual role played by crossover in genetic algorithms. This leads to the "headless chicken" test for the usefulness of crossover to a given genetic algorithm and to serious questions about the usefulness of maintaining a population. A "reverse hillclimbing" algorithms is presented that allows the determination of details of the basin of attraction points on a landscape. These details can be used to directly compare members of a class of hillclimbing algorithms and to accurately predict how long a particular hillclimber will take to discover a given point. A connection between evolutionary algorithms and the heuristic search algorithms of Artificial Intelligence and Operations Research is established. One aspect of this correspondence is investigated in detail: the relationship between fitness functions and heuristic functions. By considering how closely fitness functions approximate the ideal heuristic functions, a measure of search difficult is obtained. The measure, fitness distance correlation, is a remarkably reliableble indicator of problem difficulty for a genetic algorithm on many problems taken from the genetic algorithms literature, even though the measure incorporates no knowledge of the operation of a genetic algorithm. This leads to one answer to the question "What makes a problem hard (or easy) for a genetic algorithm?" The answer is perfectly in keeping with what has been well known in Artificial Intelligence for over thirty years.

A GENETIC ALGORITHM FOR THE JOB SHOP PROBLEMf

Abstract: Scope and Purpoee--Job shop scheduling is a strongly NP-hard problem of combinatorial optimization and one of the most well-known machine scheduling problems. Scope of this paper is to present some improvements obtained in dealing with this problem using a heuristic technique based on Genetic Algorithms.

Genetic algorithms in optimization of system reliability

Abstract: After initial production, improvements are often made to components of a system, to upgrade system performance; for example, when designing a later version or release. This paper presents an optimization model that identifies the types of component improvements and the level of effort spent on those improvements to maximize one or more performance measures (e.g., system reliability or availability) subject to constraints (e.g., cost) in the presence of uncertainty about the component failure rates. For each component failure mode, some possible improvements are identified along with their cost and the resulting improvement in failure rates for that failure mode. The objective function is defined as a stochastic function of the performance measure of interest-in this case, 5/sup th/ percentile of the mean time-between-failure distribution. The problem formulation is combinatorial and stochastic. Genetic algorithms are used as the solution method. Our approach is demonstrated on a case study of a personal computer system. Results and comparison with enumeration of the configuration space show that genetic algorithms perform very favorably in the face of noise in the output: they are able to find the optimum over a complicated, high dimensional, nonlinear space in a tiny fraction of the time required for enumeration. The integration of genetic algorithm optimization capabilities with reliability analysis can provide a robust, powerful design-for-reliability tool. >

Using Genetic Algorithms to Solve a Multiobjective Groundwater Monitoring Problem

Abstract: This paper builds on the work of Meyer and Brill (1988) and subsequent work by Meyer et al. (1990, 1992) on the optimal location of a network of groundwater monitoring wells under conditions of uncertainty. We investigate a method of optimization using genetic algorithms (GAs) which allows us to consider the two objectives of Meyer et al. (1992), maximizing reliability and minimizing contaminated area at the time of first detection, separately yet simultaneously. The GA-based solution method has the advantage of being able to generate both convex and nonconvex points of the trade-off curve, accommodate nonlinearities in the two objective functions, and not be restricted by the peculiarities of a weighted objective function. Furthermore, GAs have the ability to generate large portions of the trade-off curve in a single iteration and may be more efficient than methods that generate only a single point at a time. Four different codings of genetic algorithms are investigated, and their performance in generating the multiobjective trade-off curve is evaluated for the groundwater monitoring problem using an example data set. The GA formulations are compared with each other and also with simulated annealing on both performance and computational intensity. Simulated annealing relies on a weighted objective function which can find only a single point along the trade-off curve for each iteration, while all of the multiple-objective GA formulations are able to find a larger number of convex and nonconvex points of trade-off curve in a single iteration. Each iteration of simulated annealing is approximately five times faster than an iteration of the genetic algorithm, but several simulated annealing iterations are required to generate a trade-off curve. GAs are able to find a larger number of nondominated points on the trade-off curve, while simulated annealing finds fewer points but with a wider range of objective function values. None of the GA formulations demonstrated the ability to generate the entire trade-off curve in a single iteration. Through manipulation of GA parameters certain sections of the trade-off curve can be targeted for better performance, but as performance improves at one section it suffers at another. Run times for all GA formulations were similar in magnitude.

A Greedy Genetic Algorithm for the Quadratic Assignment Problem

Abstract: The Quadratic Assignment Problem (QAP) is one of the classical combinatorial optimization problems and is known for its diverse applications. Applications of QAP include assignments of tools to robots in flexible manufacturing systems, allocation of blades to hydraulic turbine runners, sequencing problems in production systems, and placement problem in VLSI design. In this paper, we suggest a genetic algorithm for the QAP and report its computational behaviour. The genetic algorithm incorporates many greedy principles in its design and hence, is called the greedy genetic algorithm. The ideas we incorporate in the greedy genetic algorithm include (i) generating the initial population using a randomized construction heuristic; (ii) new crossover schemes; (iii) a special purpose immigration scheme that promotes diversity; (iv) periodic local optimization of a subset of the population; (v) tournamenting among different populations; and (vi) an overall design that attempts to strike a balance between diversity and a bias towards fitter individuals. We test our algorithm on all the benchmark instances of QAPLIB, a well-known library of QAP instances. Out of the 132 total instances in QAPLIB of varied sizes, the greedy genetic algorithm obtained the best known solutions for 103 instances, and for the remaining instances (except one) found solutions within 1% of the best known solutions. Based on our computational testing, we believe that the greedy genetic algorithm is the best heuristic algorithm for dense QAP developed to date in terms of the quality of the solution.

Parallel recombinative simulated annealing: a genetic algorithm

Abstract: This paper introduces and analyzes a parallel method of simulated annealing. Borrowing from genetic algorithms, an effective combination of simulated annealing and genetic algorithms, called parallel recombinative simulated annealing, is developed. This new algorithm strives to retain the desirable asymptotic convergence properties of simulated annealing, while adding the populations approach and recombinative power of genetic algorithms. The algorithm iterates a population of solutions rather than a single solution, employing a binary recombination operator as well as a unary neighborhood operator. Proofs of global convergence are given for two variations of the algorithm. Convergence behavior is examined, and empirical distributions are compared to Boltzmann distributions. Parallel recombinative simulated annealing is amenable to straightforward implementation on SIMD, MIMD, or shared-memory machines. The algorithm, implemented on the CM-5, is run repeatedly on two deceptive problems to demonstrate the added implicit parallelism and faster convergence which can result from larger population sizes.

Adaptive image segmentation using a genetic algorithm

Abstract: We present the first closed loop image segmentation system which incorporates a genetic algorithm to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions such as time of day, time of year, clouds, etc. The segmentation problem is formulated as an optimization problem and the genetic algorithm efficiently searches the hyperspace of segmentation parameter combinations to determine the parameter set which maximizes the segmentation quality criteria. The goals of our adaptive image segmentation system are to provide continuous adaptation to normal environmental variations, to exhibit learning capabilities, and to provide robust performance when interacting with a dynamic environment. We present experimental results which demonstrate learning and the ability to adapt the segmentation performance in outdoor color imagery.

Sizing, Shape, and Topology Design Optimization of Trusses Using Genetic Algorithm

Abstract: A procedure is developed for the combined sizing, shape, and topology design of space trusses. Discrete and continuous values are used to define the cross-sectional areas of the members. The nodal locations are treated as continuous design variables using the hybrid natural approach for shape optimal design. Element connectivity and boundary conditions are treated as Boolean design variables in the context of topology design. The traditional genetic algorithm is modified to handle the problem formulation. Simple concepts are used to accelerate convergence and reduce the computational effort. Numerical examples are solved to illustrate the proposed methodology. Several conclusions drawn from the research results are presented along with some thoughts on computational strategies.

Refined genetic algorithm-economic dispatch example

Abstract: A genetic-based algorithm is used to solve a power system economic dispatch (ED) problem. The algorithm utilizes payoff information of perspective solutions to evaluate optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are eliminated. Using an economic dispatch problem as a basis for comparison, several different techniques which enhance program efficiency and accuracy, such as mutation prediction, elitism, interval approximation and penalty factors, are explored. Two unique genetic algorithms are also compared. The results are verified for a sample problem using a classical technique. >

Genetic Algorithms for Estimation Problems With Multiple Optima, Nondifferentiability, and Other Irregular Features

Abstract: The genetic algorithm is examined as a method for solving optimization problems in econometric estimation. It does not restrict either the form or regularity of the objective function, allows a reasonably large parameter space, and does not rely on a point-to-point search. The performance is evaluated through two sets of experiments on standard test problems as well as econometric problems from the literature. First, alternative genetic algorithms that vary over mutation and crossover rates, population sizes, and other features are contrasted. Second, the genetic algorithm is compared to Nelder–Mead simplex, simulated annealing, adaptive random search, and MSCORE.

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.

A hybrid approach to modeling metabolic systems using a genetic algorithm and simplex method

Abstract: One of the main obstacles in applying genetic algorithms (GA's) to complex problems has been the high computational cost due to their slow convergence rate. We encountered such a difficulty in our attempt to use the classical GA for estimating parameters of a metabolic model. To alleviate this difficulty, we developed a hybrid approach that combines a GA with a stochastic variant of the simplex method in function optimization. Our motivation for developing the stochastic simplex method is to introduce a cost-effective exploration component into the conventional simplex method. In an attempt to make effective use of the simplex operation in a hybrid GA framework, we used an elite-based hybrid architecture that applies one simplex step to a top portion of the ranked population. We compared our approach with five alternative optimization techniques including a simplex-GA hybrid independently developed by Renders-Bersini (R-B) and adaptive simulated annealing (ASA). Our empirical evaluations showed that our hybrid approach for the metabolic modeling problem outperformed all other techniques in terms of accuracy and convergence rate. We used two additional function optimization problems to compare our approach with the five alternative methods.

Multiobjective genetic algorithms with application to control engineering problems.

Abstract: Genetic algorithms (GAs) are stochastic search techniques inspired by the principles of natural selection and natural genetics which have revealed a number of characteristics particularly useful for applications in optimization, engineering, and computer science, among other fields. In control engineering, they have found application mainly in problems involving functions difficult to characterize mathematically or known to present difficulties to more conventional numerical optimizers, as well as problems involving non-numeric and mixed-type variables. In addition, they exhibit a large degree of parallelism, making it possible to effectively exploit the computing power made available through parallel processing. Despite their early recognized potential for multiobjective optimization (almost all engineering problems involve multiple, often conflicting objectives), genetic algorithms have, for the most part, been applied to aggregations of the objectives in a single-objective fashion, like conventional optimizers. Although alternative approaches based on the notion of Pareto-dominance have been suggested, multiobjective optimization with genetic algorithms has received comparatively little attention in the literature. In this work, multiobjective optimization with genetic algorithms is reinterpreted as a sequence of decision making problems interleaved with search steps, in order to accommodate previous work in the field. A unified approach to multiple objective and constraint handling with genetic algorithms is then developed from a decision making perspective and characterized, with application to control system design in mind. Related genetic algorithm issues, such as the ability to maintain diverse solutions along the trade-off surface and responsiveness to on-line changes in decision policy, are also considered. The application of the multiobjective GA to three realistic problems in optimal controller design and non-linear system identification demonstrates the ability of the approach to concurrently produce many good compromise solutions in a single run, while making use of any preference information interactively supplied by a human decision maker. The generality of the approach is made clear by the very different nature of the two classes of problems considered.

Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms

Abstract: A measure of search difficulty, fitness distance correlation (FDC), is introduced and its power as a predictor of genetic algorithm (GA) performance is investigated. The sign and magnitude of this correlation can be used to predict the performance of a GA on many problems where the global maxima are already known. FDC can be used to correctly classify easy deceptive problems and easy and difficult non-deceptive problems as difficult, it can be used to indicate when Gray coding will prove better than binary coding, it produces the expected answers when applied to problems over a wide range of apparent difficulty, and it is also consistent with the surprises encountered when GAs were used on the Tanese and royal road functions. The FDC measure is a consequence of an investigation into the connection between GAs and heuristic search.

Search-intensive concept induction

Abstract: This paper describes REGAL, a distributed genetic algorithm-based system, designed for learning first-order logic concept descriptions from examples. The system is a hybrid of the Pittsburgh and the Michigan approaches, as the population constitutes a redundant set of partial concept descriptions, each evolved separately. In order to increase effectiveness, REGAL is specifically tailored to the concept learning task; hence, REGAL is task-dependent, but, on the other hand, domain-independent. The system proved particularly robust with respect to parameter setting across a variety of different application domains. REGAL is based on a selection operator, called Universal Suffrage operator, provably allowing the population to asymptotically converge, on the average, to an equilibrium state in which several species coexist. The system is presented in both a serial and a parallel version, and a new distributed computational model is proposed and discussed. The system has been tested on a simple artificial domain for the sake of illustration, and on several complex real-world and artificial domains in order to show its power and to analyze its behavior under various conditions. The results obtained so far suggest that genetic search may be a valuable alternative to logic-based approaches to learning concepts, when no (or little) a priori knowledge is available and a very large hypothesis space has to be explored.

Optimization method for reactive power planning by using a modified simple genetic algorithm

Abstract: This paper presents an improved simple genetic algorithm developed for reactive power system planning. Successive linear programming is used to solve operational optimization sub-problems. A new population selection and generation method which makes the use of Benders' cut is presented in this paper. It is desirable to find the optimal solution in few iterations, especially in some test cases where the optimal results are expected to be obtained easily. However, the simple genetic algorithm has failed in finding the solution except through an extensive number of iterations. Different population generation and crossover methods are also tested and discussed. The method has been tested for 6 bus and 30 bus power systems to show its effectiveness. Further improvement for the method is also discussed.