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18
Applying Genetic Algorithms to Multiobjective Land Use Planning
 W3C Recommendation 22
, 2000
"... k.matthews, a.r.sibbald @ mluri.sari.ac.uk This paper explores the application of multiobjective Genetic Algorithms (mGAs) to rural land use planning, a spatial allocation problem. Two mGAs are proposed. Both share an underlying structure of: fitness assignment using Paretodominance ranking, niche ..."
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k.matthews, a.r.sibbald @ mluri.sari.ac.uk This paper explores the application of multiobjective Genetic Algorithms (mGAs) to rural land use planning, a spatial allocation problem. Two mGAs are proposed. Both share an underlying structure of: fitness assignment using Paretodominance ranking, niche induction and an individual replacement strategy. They are differentiated by their representations: a fixedlength genotype composed of genes that map directly to a land parcel's use and a variablelength, orderdependent representation making allocations indirectly via a greedy algorithm. The latter representation requires additional breeding operators to be defined and postprocessing of the genotype structure to identify and remove duplicate genotypes. The two mGAs are compared on a real land use planning problem and the strengths and weaknesses of the underlying framework and each representation are identified. 1
Multiparent Recombination in Genetic Algorithms with Search Space Boundary Extension by Mirroring
 Proc. of the Fifth International Conference on Parallel Problem Solving from Nature (PPSN V
, 1998
"... Abstract. In previous work, we have investigated real coded genetic algorithms with several types of multiparent recombination operators and found evidence that multiparent recombination with center of mass crossover (CMX) seems a good choice for real coded GAs. But CMX does not work well on funct ..."
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Abstract. In previous work, we have investigated real coded genetic algorithms with several types of multiparent recombination operators and found evidence that multiparent recombination with center of mass crossover (CMX) seems a good choice for real coded GAs. But CMX does not work well on functions which have their optimum on the corner of the search space. In this paper, we propose a method named boundary extension by mirroring (BEM) to cope with this problem. Applying BEM to CMX, the performance of CMX on the test functions which have their optimum on the corner of the search space was much improved. Further, by applying BEM, we observed clear improvement in performance of twoparent recombination on the functions which have their optimum on the corner of the search space. Thus, we suggest that BEM is a good general technique to improve the efficiency of crossover operators in realcoded GAs for a wide range of functions. 1.
Search Space Boundary Extension Method in RealCoded Genetic Algorithms
 Information Sciences
, 2001
"... In realcoded genetic algorithms, some crossover operators do not work well on functions which have their optimum at the corner of the search space. To cope with this problem, we have proposed a boundary extension methods which allows individuals to be located within a limited space beyond the bound ..."
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In realcoded genetic algorithms, some crossover operators do not work well on functions which have their optimum at the corner of the search space. To cope with this problem, we have proposed a boundary extension methods which allows individuals to be located within a limited space beyond the boundary of the search space. In this paper, we give an analysis of the boundary extension methods from the view point of sampling bias and perform a comparative study on the effect of applying two boundary extension methods, namely the boundary extension by mirroring BEM) and the boundary extension with extended selection (BES). We were able to confirm that to use sampling methods which have smaller sampling bias had good performance on both functions which have their optimum at or near the boundaries of the search space, and functions which have their optimum at the center of the search space. The BES/SD/A (BES by shortest distance selection with aging) had good performance on functions which have their optimum at or near the boundaries of the search space. We also confirmed that applying the BES/SD/A did not cause any performance degradation on functions which have their optimum at the center of the search space. 1.
An Efficient Modified Artificial Bee Colony Algorithm for Job Scheduling Problem
 International Journal of Soft Computing and Engineering (IJSCE) ISSN: 22312307, Volume1, Issue6
, 2012
"... Abstract Swarm intelligence systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. Particle swarm, Ant colony, Bee colony are examples of swarm intelligence. In the field of computer science and operations research, A ..."
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Abstract Swarm intelligence systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. Particle swarm, Ant colony, Bee colony are examples of swarm intelligence. In the field of computer science and operations research, Artificial Bee Colony Algorithm (ABC) is an optimization algorithm based on the intelligent foraging behavior of honey bee swarm. The job scheduling problem is the problem of assigning the jobs in the system in a manner that will optimize the overall performance of the application, while assuring the correctness of the result. In this paper, An Efficient artificial bee colony (ABC) algorithm, where we have used additional mutation and crossover operator of Genetic algorithm (GA) in the classical ABC algorithm. We have added crossover operator after the employed bee phase and mutation operator after onlooker bee phase of ABC algorithm, is proposed in this paper, for solving the job scheduling problem with the criterion to decrease the maximum completion time. The simulated results show that ABC proves to be a better algorithm when applied to job scheduling problem.
Unit Commitment In Thermal Power Generation Using Genetic Algorithms
 In Proceedings of the Sixth International Conference on Industrial & Engineering Applications of Arti Intelligence and Expert Systems (IEA/AIE93
, 1993
"... Unit commitment is a complex decisionmaking process because of multiple constraints which must not be violated while finding the optimal or a nearoptimal commitment schedule. This paper discusses the application of genetic algorithms for determining shortterm commitment of thermal units in power ..."
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Unit commitment is a complex decisionmaking process because of multiple constraints which must not be violated while finding the optimal or a nearoptimal commitment schedule. This paper discusses the application of genetic algorithms for determining shortterm commitment of thermal units in power generation. The objective of the optimal commitment is to determine the on/off states of the units in the system to meet the load demand and spinningreserve requirement at each time period such that the overall cost of generation is minimum, while satisfying various operational constraints. The paper examines the feasibility of using genetic algorithms, and reports preliminary results in determining a nearoptimal commitment order of thermal units in studied power systems. INTRODUCTION In power industries, fuel expenses constitute a significant part of the overall generation costs. In general, there exist different types of thermal power units based on the fuel used (e.g coal, natural gas,...
Neural network weight selection using genetic algorithms
 Intelligent Hybrid Systems
, 1995
"... Neural networks are a computational paradigm modeled on the human brain that has become popular in ..."
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Neural networks are a computational paradigm modeled on the human brain that has become popular in
Genetic Search of a Generalized Hough Transform Space
 In preparation
, 1994
"... We use a Generalized Hough transform (GHT) to detect and track instances of a class of sonar signals. This class consists of a fourdimensional set of curves and hence requires a fourdimensional transform space for the GHT. Many of the signals we need to detect are very weak. Such signals yield pea ..."
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Cited by 2 (2 self)
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We use a Generalized Hough transform (GHT) to detect and track instances of a class of sonar signals. This class consists of a fourdimensional set of curves and hence requires a fourdimensional transform space for the GHT. Many of the signals we need to detect are very weak. Such signals yield peaks in the transform space which are both very narrow and not too far above the random background variations. Finding such peaks is difficult. Exhaustive search over a predetermined discretization of the transform space will yield a nearly optimal point for a sufficiently fine discretization. However, even with an intelligently chosen discretization, exhaustive search requires searching over (and hence evaluating) many points in the transform space. We have therefore developed a genetic algorithm to more efficiently search the transform space. Designing the genetic algorithm to work properly has required experimentation with a number of its parameters. The most important of these are (i) the representation, (ii) the population size, and (iii) the number of runs.
Genetic Optimization of the Parameters of a TrackWhileDetect Algorithm,” in these proceedings
"... We have developed an algorithm to detect the presence of narrowband signals and track the time evolution of their center frequencies. This algorithm has 35 parameters whose optimal values depend on (among other things): (1) the expected dynamics of the signals, (ii) the background statistics, and (i ..."
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We have developed an algorithm to detect the presence of narrowband signals and track the time evolution of their center frequencies. This algorithm has 35 parameters whose optimal values depend on (among other things): (1) the expected dynamics of the signals, (ii) the background statistics, and (iii) the clutter (i.e., the number of simultaneous signals). Manually optimizing these parameters is a difficult task not only because of the large number of parameters but also because of the interdependence of their effects on performance. We have therefore devised an automated method for optimizing the parameters. It has three basic components: (i) a ”truth ” database with a graphical interface for easy manual entry of ”truth”, (ii) a scoring function which is a linear combination of six subscores (three evaluating detection performance and three evaluating tracking performance), and (iii) a distributed genetic algorithm which optimizes the parameter values for a particular truth database. We have used this procedure to optimize the parameter values to a variety of signal types and environmental conditions. The results have been improved performance as well as the ability to make the algorithm adaptive: as the system detects changes in the environmental conditions, it can switch to a different set of parameters. 1.1 Detection and tracking of narrowband signals 1.
Key Generation for Text Encryption in Cellular Networks using Multipoint Crossover Function
"... This paper is mainly concerned with providing security for messages in cellular networks. Encryption of data in cellular networks is mandatory since it is sensitive to eaves dropping. This project focuses on encrypting the data sent between the mobile stations and base stations using a stream cipher ..."
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This paper is mainly concerned with providing security for messages in cellular networks. Encryption of data in cellular networks is mandatory since it is sensitive to eaves dropping. This project focuses on encrypting the data sent between the mobile stations and base stations using a stream cipher method. However, the keys for encryption are generated using an evolutionary computation approach termed genetic algorithm. This genetic algorithm technique gives the best or optimal key for encryption. Before we single point cross over technique is used in generating optimal key for encryption but this paper emphasizes on genetic algorithm technique for different sizes of population and different number of iterations considering multi point crossover. The plain text which is to be encrypted along with the key are encoded using the arithmetic coding technique. Encryption is done to convert the plain text into cipher text. And the comparison with the existing system is explained in detail.
On the Effect of Multiparents Recombination in Binary Coded Genetic Algorithms
 2ND INTERNATIONAL CONFERENCE ON KNOWLEDGEBASED INTELLIGENT ELECTRONIC SYSTEMS (KES98)
, 1998
"... Recombination operator plays a very important role in genetic algorithms. In this paper, we present binary coded genetic algorithms in which more than two parents are involved in recombination operation. We propose two types of multiparent recombination operators, the multicut (MX) and seed crosso ..."
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Recombination operator plays a very important role in genetic algorithms. In this paper, we present binary coded genetic algorithms in which more than two parents are involved in recombination operation. We propose two types of multiparent recombination operators, the multicut (MX) and seed crossover (SX). Each of these operators is a natural generalization of two parents recombination operator. These operators are evaluated on the De Jong standard test functions. The results showed clearly that the multiparent recombinations lead to better performance, although the performance improvement for different techniques were found to be dependent on problems.