Showing papers on "Genetic algorithm published in 2000"

A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II

Abstract: Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN3) computational complexity (where M is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algorithm (we called it the Non-dominated Sorting GA-II or NSGA-II) which alleviates all the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN2) computational complexity is presented. Second, a selection operator is presented which creates a mating pool by combining the parent and child populations and selecting the best (with respect to fitness and spread) N solutions. Simulation results on five difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA--two other elitist multi-objective EAs which pay special attention towards creating a diverse Pareto-optimal front. Because of NSGA-II's low computational requirements, elitist approach, and parameter-less sharing approach, NSGA-II should find increasing applications in the years to come.

Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy

Abstract: We introduce a simple evolution scheme for multiobjective optimization problems, called the Pareto Archived Evolution Strategy (PAES) We argue that PAES may represent the simplest possible nontrivial algorithm capable of generating diverse solutions in the Pareto optimal set The algorithm, in its simplest form, is a (1 + 1) evolution strategy employing local search but using a reference archive of previously found solutions in order to identify the approximate dominance ranking of the current and candidate solution vectors (1 + 1)-PAES is intended to be a baseline approach against which more involved methods may be compared It may also serve well in some real-world applications when local search seems superior to or competitive with population-based methods We introduce (1 + λ) and (μ | λ) variants of PAES as extensions to the basic algorithm Six variants of PAES are compared to variants of the Niched Pareto Genetic Algorithm and the Nondominated Sorting Genetic Algorithm over a diverse suite of six test functions Results are analyzed and presented using techniques that reduce the attainment surfaces generated from several optimization runs into a set of univariate distributions This allows standard statistical analysis to be carried out for comparative purposes Our results provide strong evidence that PAES performs consistently well on a range of multiobjective optimization tasks

An updated survey of GA-based multiobjective optimization techniques

Abstract: After using evolutionary techniques for single-objective optimization during more than two decades, the incorporation of more than one objective in the fitness function has finally become a popular area of research. As a consequence, many new evolutionary-based approaches and variations of existing techniques have recently been published in the technical literature. The purpose of this paper is to summarize and organize the information on these current approaches, emphasizing the importance of analyzing the operations research techniques in which most of them are based, in an attempt to motivate researchers to look into these mathematical programming approaches for new ways of exploiting the search capabilities of evolutionary algorithms. Furthermore, a summary of the main algorithms behind these approaches is provided, together with a brief criticism that includes their advantages and disadvantages, degree of applicability, and some known applications. Finally, further trends in this area and some possible paths for further research are also addressed.

Ant colony optimization for resource-constrained project scheduling

Abstract: An ant colony optimization approach (ACO) for the resource-constrained project scheduling problem (RCPSP) is presented. Combinations of two pheromone evaluation methods are used by the ants to find new solutions. We tested our ACO algorithm on a set of large benchmark problems from the PSPLIB. Compared to several other heuristics for the RCPSP including genetic algorithms, simulated annealing, tabu search, and different sampling methods our algorithm performed best on the average. For some test instances the algorithm was able to find new best solutions.

Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index

Abstract: This paper proposes genetic algorithms (GAs) approach to feature discretization and the determination of connection weights for artificial neural networks (ANNs) to predict the stock price index. Previous research proposed many hybrid models of ANN and GA for the method of training the network, feature subset selection, and topology optimization. In most of these studies, however, GA is only used to improve the learning algorithm itself. In this study, GA is employed not only to improve the learning algorithm, but also to reduce the complexity in feature space. GA optimizes simultaneously the connection weights between layers and the thresholds for feature discretization. The genetically evolved weights mitigate the well-known limitations of the gradient descent algorithm. In addition, globally searched feature discretization reduces the dimensionality of the feature space and eliminates irrelevant factors. Experimental results show that GA approach to the feature discretization model outperforms the other two conventional models.

Application of genetic algorithm–PLS for feature selection in spectral data sets

Abstract: After suitable modifications, genetic algorithms can be a useful tool in the problem of wavelength selection in the case of a multivariate calibration performed by PLS. Unlike what happens with the majority of feature selection methods applied to spectral data, the variables selected by the algorithm often correspond to well-defined and characteristic spectral regions instead of being single variables scattered throughout the spectrum. This leads to a model having a better predictive ability than the full-spectrum model; furthermore, the analysis of the selected regions can be a valuable help in understanding which are the relevant parts of the spectra. After the presentation of the algorithm, several real cases are shown. Copyright © 2000 John Wiley & Sons, Ltd.

A novel genetic algorithm based on immunity

Abstract: A novel algorithm, the immune genetic algorithm (IGA), is proposed based on the theory of immunity in biology which mainly constructs an immune operator accomplished by two steps: 1) a vaccination and 2) an immune selection. IGA proves theoretically convergent with probability 1. Strategies and methods of selecting vaccines and constructing an immune operator are also given. IGA is illustrated to be able to restrain the degenerate phenomenon effectively during the evolutionary process with examples of TSP, and can improve the searching ability and adaptability, greatly increase the convergence rate.

Nature's heuristics for scheduling jobs on Computational Grids

Abstract: Computational Grid (Grid Computing) is a new paradigm that will drive the computing arena in the new millennium. Unification of globally remote and diverse resources, coupled with the increasing computational needs for Grand Challenge Applications (GCA) and accelerated growth of the Internet and communication technology will further fuel the development of global computational power grids. In this paper, we attempt to address the scheduling of jobs to the geographically distributed computing resources. Conventional wisdom in the field of scheduling is that scheduling problems exhibit such richness and variety that no single scheduling method is sufficient. Heuristics derived from the nature has demonstrated a surprising degree of effectiveness and generality for handling combinatorial optimization problems. This paper begins with an introduction of computational grids followed by a brief description of the three nature's heuristics namely Genetic Algorithm (GA), Simulated Annealing (SA) and Tabu Search (TS). Experimental results using GA are included. We further demonstrate the hybridized usage of the above algorithms that can be applied in a computational grid environment for job scheduling.

Constraint-handling using an evolutionary multiobjective optimization technique

Abstract: In this paper, we introduce the concept of non-dominance (commonly used in multi-objective optimization) as a way to incorporate constraints into the fitness function of a genetic algorithm Each individual is assigned a rank based on its degree of dominance over the rest of the population Feasible individuals are always ranked higher than infeasible ones, and the degree of constraint violation determines the rank among infeasible individuals The proposed technique does not require fine tuning of factors like the traditional penalty function and uses a self-adaptation mechanism that avoids the traditional empirical adjustment of the main genetic operators (ie, crossover and mutation)

Linkage Problem, Distribution Estimation, and Bayesian Networks

Abstract: This paper proposes an algorithm that uses an estimation of the joint distribution of promising solutions in order to generate new candidate solutions. The algorithm is settled into the context of genetic and evolutionary computation and the algorithms based on the estimation of distributions. The proposed algorithm is called the Bayesian Optimization Algorithm (BOA). To estimate the distribution of promising solutions, the techniques for modeling multivariate data by Bayesian networks are used. The BOA identifies, reproduces, and mixes building blocks up to a specified order. It is independent of the ordering of the variables in strings representing the solutions. Moreover, prior information about the problem can be incorporated into the algorithm, but it is not essential. First experiments were done with additively decomposable problems with both nonoverlapping as well as overlapping building blocks. The proposed algorithm is able to solve all but one of the tested problems in linear or close to linear time with respect to the problem size. Except for the maximal order of interactions to be covered, the algorithm does not use any prior knowledge about the problem. The BOA represents a step toward alleviating the problem of identifying and mixing building blocks correctly to obtain good solutions for problems with very limited domain information.

Evolutionary artificial potential fields and their application in real time robot path planning

Abstract: A new methodology named Evolutionary Artificial Potential Field (EAPF) is proposed for real-time robot path planning. The artificial potential field method is combined with genetic algorithms, to derive optimal potential field functions. The proposed EAPF approach is capable of navigating robot(s) situated among moving obstacles. Potential field functions for obstacles and goal points are also defined. The potential field functions for obstacles contain tunable parameters. The multi-objective evolutionary algorithm (MOEA) is utilized to identify the optimal potential field functions. Fitness functions such as goal-factor, obstacle-factor, smoothness-factor and minimum-pathlength-factor are developed for the MOEA selection criteria. An algorithm named escape-force is introduced to avoid the local minima associated with EAPF. Moving obstacles and moving goal positions were considered to test the robust performance of the proposed methodology. Simulation results show that the proposed methodology is efficient and robust for robot path planning with non-stationary goals and obstacles.

A Continuous Genetic Algorithm Designed for the Global Optimization of Multimodal Functions

Abstract: Genetic algorithms are stochastic search approaches based on randomized operators, such as selection, crossover and mutation, inspired by the natural reproduction and evolution of the living creatures. However, few published works deal with their application to the global optimization of functions depending on continuous variables. A new algorithm called Continuous Genetic Algorithm (CGA) is proposed for the global optimization of multiminima functions. In order to cover a wide domain of possible solutions, our algorithm first takes care over the choice of the initial population. Then it locates the most promising area of the solution space, and continues the search through an “intensification” inside this area. The selection, the crossover and the mutation are performed by using the decimal code. The efficiency of CGA is tested in detail through a set of benchmark multimodal functions, of which global and local minima are known. CGA is compared to Tabu Search and Simulated Annealing, as alternative algorithms.

Genetic-Algorithms-Based Approach for Bilevel Programming Models

Abstract: Many decision-making problems in transportation system planning and management can be formulated as bilevel programming models, which are intrinsically nonconvex and hence difficult to solve for the global optimum. Therefore, successful implementations of bilevel models rely largely on the development of an efficient algorithm in handling realistic complications. In spite of various intriguing attempts that were made in solving the bilevel models, these algorithms are unfortunately either incapable of finding the global optimum or very computationally intensive and impractical for problems of a realistic size. In this paper, a genetic-algorithms-based (GAB) approach is proposed to efficiently solve these models. The performance of the algorithm is illustrated and compared with the previous sensitivity-analysis-based algorithm using numerical examples. The computation results show that the GAB approach is efficient and much simpler than previous heuristic algorithms. Furthermore, it is believed that the GAB approach can more likely achieve the global optimum based on the globality and parallelism of genetic algorithms.

Probabilistic neural-network structure determination for pattern classification

Abstract: Network structure determination is an important issue in pattern classification based on a probabilistic neural network. In this study, a supervised network structure determination algorithm is proposed. The proposed algorithm consists of two parts and runs in an iterative way. The first part identifies an appropriate smoothing parameter using a genetic algorithm, while the second part determines suitable pattern layer neurons using a forward regression orthogonal algorithm. The proposed algorithm is capable of offering a fairly small network structure with satisfactory classification accuracy.

Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons

Abstract: The use of intelligent techniques in the manufacturing field has been growing the last decades due to the fact that most manufacturing optimization problems are combinatorial and NP hard. This paper examines recent developments in the field of evolutionary computation for manufacturing optimization. Significant papers in various areas are highlighted, and comparisons of results are given wherever data are available. A wide range of problems is covered, from job shop and flow shop scheduling, to process planning and assembly line balancing.

Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem

Abstract: There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem.

Three Scheduling Algorithms Applied to the Earth Observing Systems Domain

Abstract: This paper describes three approaches to assigning tasks to earth observing satellites EOS. A fast and simple priority dispatch method is described and shown to produce acceptable schedules most of the time. A look ahead algorithm is then introduced that outperforms the dispatcher by about 12% with only a small increase in run time. These algorithms set the stage for the introduction of a genetic algorithm that uses job permutations as the population. The genetic approach presented here is novel in that it uses two additional binary variables, one to allow the dispatcher to occasionally skip a job in the queue and another to allow the dispatcher to occasionally allocate the worst position to the job. These variables are included in the recombination step in a natural way. The resulting schedules improve on the look ahead by as much as 15% at times and 3% on average. We define and use the "window-constrained packing" problem to model the bare bones of the EOS scheduling problem.

Design of Nonlinear Framed Structures Using Genetic Optimization

Abstract: In this paper we present a genetic algorithm (GA)-based optimization procedure for the design of 2D, geometrical, nonlinear steel-framed structures. The approach presented uses GAs as a tool to achieve discrete nonlinear optimal or near-optimal designs. Frames are designed in accordance with the requirements of the AISC-LRFD specification. In this paper, we employ a group selection mechanism, discuss an improved adapting crossover operator, and provide recommendations on the penalty function selection. We compare the differences between optimized designs obtained by linear and geometrically nonlinear analyses. Through two examples, we will illustrate that the optimal designs are not affected significantly by the P-Δ effects. However, in some cases we may achieve a better design by performing nonlinear analysis instead of linear analysis.

An improved genetic algorithm for generation expansion planning

Abstract: This paper presents a development of an improved genetic algorithm (IGA) and its application to a least-cost generation expansion planning (GEP) problem. Least-cost GEP problem is concerned with a highly constrained nonlinear dynamic optimization problem that can only be fully solved by complete enumeration, a process which is computationally impossible in a real-world GEP problem. In this paper, an improved genetic algorithm incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. The main advantage of the IGA approach is that the "curse of dimensionality" and a local optimal trap inherent in mathematical programming methods can be simultaneously overcome. The IGA approach is applied to two test systems, one with 15 existing power plants, 5 types of candidate plants and a 14-year planning period, and the other, a practical long-term system with a 24-year planning period.