Computer Science
Genetic Algorithm
Genetic Algorithm is a computational technique inspired by the process of natural selection and evolution. It involves generating a population of potential solutions to a problem, then using selection, crossover, and mutation operations to evolve and improve these solutions over successive generations. This approach is often used to find optimal or near-optimal solutions to complex optimization and search problems.
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10 Key excerpts on "Genetic Algorithm"
No longer available |Learn more- (Author)
- 2014(Publication Date)
- University Publications(Publisher)
________________________ WORLD TECHNOLOGIES ________________________ Chapter- 9 Genetic Algorithm The Genetic Algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic Algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. Methodology In a Genetic Algorithm, a population of strings (called chromosomes or the genotype of the genome), which encode candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem, evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. If the algorithm has terminated due to a maximum number of generations, a satisfactory solution may or may not have been reached. Genetic Algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics and other fields. A typical Genetic Algorithm requires: 1. a genetic representation of the solution domain, 2.
No longer available |Learn more- (Author)
- 2014(Publication Date)
- University Publications(Publisher)
________________________ WORLD TECHNOLOGIES ________________________ Chapter- 7 Genetic Algorithm The Genetic Algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic Algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. Methodology In a Genetic Algorithm, a population of strings (called chromosomes or the genotype of the genome), which encode candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem, evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. If the algorithm has terminated due to a maximum number of generations, a satisfactory solution may or may not have been reached. Genetic Algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics and other fields. A typical Genetic Algorithm requires: 1. a genetic representation of the solution domain, 2. a fitness function to evaluate the solution domain.
No longer available |Learn more- (Author)
- 2014(Publication Date)
- University Publications(Publisher)
________________________ WORLD TECHNOLOGIES ________________________ Chapter- 1 Genetic Algorithm The Genetic Algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic Algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. Methodology In a Genetic Algorithm, a population of strings (called chromosomes or the genotype of the genome), which encode candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem, evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. If the algorithm has terminated due to a maximum number of generations, a satisfactory solution may or may not have been reached. Genetic Algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics and other fields. A typical Genetic Algorithm requires: 1. a genetic representation of the solution domain, 2. a fitness function to evaluate the solution domain.
No longer available |Learn more- (Author)
- 2014(Publication Date)
- University Publications(Publisher)
________________________ WORLD TECHNOLOGIES ________________________ Chapter- 7 Evolutionary Algorithm Techniques Genetic Algorithm The Genetic Algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic Algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. Methodology In a Genetic Algorithm, a population of strings (called chromosomes or the genotype of the genome), which encode candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem, evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. If the algorithm has terminated due to a maximum number of generations, a satisfactory solution may or may not have been reached. Genetic Algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics,chemistry,manufacturing,mathematics,physics and other fields. A typical Genetic Algorithm requires: ________________________ WORLD TECHNOLOGIES ________________________ 1.
No longer available |Learn more- (Author)
- 2014(Publication Date)
- College Publishing House(Publisher)
WT ____________________ WORLD TECHNOLOGIES ____________________ Chapter 7 Genetic Algorithm The Genetic Algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic Algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. Methodology In a Genetic Algorithm, a population of strings (called chromosomes or the genotype of the genome), which encode candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem, evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. If the algorithm has terminated due to a maximum number of generations, a satisfactory solution may or may not have been reached. Genetic Algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics and other fields. A typical Genetic Algorithm requires: 1. a genetic representation of the solution domain, 2.
eBook - PDFVision Systems
Segmentation and Pattern Recognition
- Goro Obinata, Ashish Dutta, Goro Obinata, Ashish Dutta(Authors)
- 2007(Publication Date)
- IntechOpen(Publisher)
21 Genetic Algorithms: Basic Ideas, Variants and Analysis Sharapov R.R. Institute of Mathematics and Mechanics (IMM) Russia 1. Introduction Genetic Algorithms are wide class of global optimization methods. As well as neural networks and simulated annealing, Genetic Algorithms are an example of successful using of interdisciplinary approach in mathematics and computer science. Genetic Algorithm simulates natural selection and evolution process, which are well studied in biology. In most cases, however, Genetic Algorithms are nothing else than probabilistic methods, which are based on principles of evolution. The idea of Genetic Algorithm appears first in 1967 in J. D. Bagley’s thesis (Bagley, 1967). The theory and applicability was then strongly influenced by J. H. Holland, who can be considered as the pioneer of Genetic Algorithms (Holland, 1992). Since then, this field has witnessed a tremendous development. There are many applications where Genetic Algorithms are used. Wide spectrum of problems from various branches of knowledge can be considered as optimization problem. This problem appears in economics and finances, cybernetics and process control, game theory, pattern recognition and image analysis, cluster analysis etc. Also Genetic Algorithm can be adapted for multicriterion optimization task for Pareto-optimal solutions search. But most popular applications of Genetic Algorithm are still neural networks learning and fuzzy knowledge base generation. There are three ways in using Genetic Algorithms with neural networks: 1. Weight learning. Optimal net weights are found with Genetic Algorithm when conventional methods (e.g. backpropagation) are not applicable. It is suitable when continuous activation function of neuron (such as sigmoid) is used, so error function become multiextremal and conventional method can find only local minimum.
eBook - PDFGenetic Algorithms and Genetic Programming
Modern Concepts and Practical Applications
- Michael Affenzeller, Stefan Wagner, Stephan Winkler, Andreas Beham(Authors)
- 2009(Publication Date)
- Chapman and Hall/CRC(Publisher)
Chapter 1 Simulating Evolution: Basics about Genetic Algorithms 1.1 The Evolution of Evolutionary Computation Work on what is nowadays called evolutionary computation started in the sixties of the 20th century in the United States and Germany. There have been two basic approaches in computer science that copy evolutionary mech-anisms: evolution strategies (ES) and Genetic Algorithms (GA). Genetic al-gorithms go back to Holland [Hol75], an American computer scientist and psychologist who developed his theory not only under the aspect of solving optimization problems but also to study self-adaptiveness in biological pro-cesses. Essentially, this is the reason why Genetic Algorithms are much closer to the biological model than evolution strategies. The theoretical foundations of evolution strategies were formed by Rechenberg and Schwefel (see for ex-ample [Rec73] or [Sch94]), whose primary goal was optimization. Although these two concepts have many aspects in common, they developed almost in-dependently from each other in the USA (where GAs were developed) and Germany (where research was done on ES). Both attempts work with a population model whereby the genetic informa-tion of each individual of a population is in general different. Among other things this genotype includes a parameter vector which contains all necessary information about the properties of a certain individual. Before the intrinsic evolutionary process takes place, the population is initialized arbitrarily; evo-lution, i.e., replacement of the old generation by a new generation, proceeds until a certain termination criterion is fulfilled. The major difference between evolution strategies and Genetic Algorithms lies in the representation of the genotype and in the way the operators are used (which are mutation, selection, and eventually recombination).
eBook - PDF- Matthias Schmidt(Author)
- 2011(Publication Date)
- IntechOpen(Publisher)
A Genetic Algorithm uses three genetic operators – reproduction , crossover and mutation (Goldberg, 2002). Many differences can be observed in the strategy of the parent selection, the form of genes, the realization of crossover operator, the replacement scheme, etc. A basic steady-state Genetic Algorithm involves the following steps. Initialization . In each step, a Genetic Algorithm contains a number of solutions (individuals) in one or more populations. Each solution is represented by genome (or chromosome). Initialization creates a starting population and sets all bits of all chromosomes to an initial (usually random) value. Crossover . The crossover is the main procedure to ensure progress of the Genetic Algorithm. The crossover operator should be implemented so that by combining several existing chromosomes a new chromosome is created, which is expected to be a better solution to the problem. Mutation . Mutation operator involves a random distortion of random chromosomes; the purpose of this operation is to overcome the tendency of Genetic Algorithm in reaching the local optimum instead of global optimum. Simple mutation is implemented so that each gene in each chromosome can be randomly changed with a certain very small probability. Finalization . The population cycle is repeated until a termination condition is satisfied. There are two basic finalization variations: maximal number of iterations and the quality of the best solution. Since the latter condition may never be satisfied both conditions are usually used. Automatic Generation of Programs 19 The critical operation of Genetic Algorithm is crossover which requires that it is possible to determine what a “better solution” is. This is determined by a fitness function (criterion function or objective function). The fitness function is the key feature of Genetic Algorithm, since the Genetic Algorithm performs the minimization of this function.
eBook - PDFBiomimetics
Biologically Inspired Technologies
- Yoseph Bar-Cohen(Author)
- 2005(Publication Date)
- CRC Press(Publisher)
When robust approaches (that guarantee finding the optimal solution) are not efficient enough to be performed in a reasonable computer time, heuristic algorithms are used. Heuristic algorithms are designed to find a ‘‘good’’ solution but do not guarantee that the optimal solution is found. In the last three decades, researchers have developed ‘‘metaheuristic’’ methods (Salhi, 1998) that are problem-independent general heuristic approaches to be applied in any optimization problem. Following a brief overview of major metaheuristic approaches, we will focus on Genetic Algorithms, also referred to as evolutionary algorithms. It is interesting to note that many of these metaheuristic methods mimic biological and other natural processes, most notably Genetic Algorithms mimic evolutionary processes, natural selection, and survival of the fittest. 5.1.1 Common Metaheuristic Methods 1. Steepest descent is the ‘‘classic’’ local search for a minimum. A ‘‘neighborhood’’ of nearby combinations is defined for each particular combination. The number of combinations in the neighborhood is usually quite small. The algorithm starts at one (usually constructed randomly) combination and proceeds iteratively. At each iteration, (i) all combinations in the neighborhood are evaluated, and (ii) if a better combination is found, the search moves to the best combination in the neighborhood and the iteration is completed. The next iteration applies the same process to the 158 Biomimetics: Biologically Inspired Technologies newly elected combination (i.e., evaluating all combinations in the new neighborhood, etc.). The algorithm terminates when there is no better combination in the neighborhood. 2. Tabu search (Glover, 1986; Glover and Laguna, 1997) is based on artificial intelligence. The search starts as a steepest descent algorithm but continues after the steepest descent algorithm has been terminated.
eBook - PDF- Anupam Shukla, Ritu Tiwari, Rahul Kala(Authors)
- 2010(Publication Date)
- CRC Press(Publisher)
The beauty of this algorithm is that it finds the best point in this infinite search space as early as possible. The amount of search space traversed in the algorithm’s run time may be a very minute proportion of the total search space. The iterative approach is thus a very use-ful tool that gives valuable solutions per the time constraints. 5.4.2 S OLUTION The GA is applied to the optimization problems, in which we are required to optimize an objective function by varying some variables or parameters. The real life problem first must be converted to Generate random solutions. While stopping criterion Generate next order generation by genetic operators. Evaluation by fitness function Return best solution. No Yes FIGURE 5.1 The simple Genetic Algorithm. Evolutionary Algorithms 151 this optimization problem before GA is applied over it. Whenever we talk of problem in this section, we are referring to this optimization problem. Solution is an individual in the population set that represents a set of parameters or variables of the problem to be optimized. The solution may be good or bad, depending on the fitness measured by the fitness function. Consider the problem of functional optimization, in which we are given a set of input variables. We have an objective function that we must optimize, which is the goal of the problem. In addition, we have a set of constraints that every solution must follow. An example of this problem is given by Equation 5.1. In this problem, a solution may be represented by two values, one corresponding to the value of a and the other corresponding to the value of b . The general form of any solution may be taken as ( a , b ). Maximize: a * b (5.1) Constraints: 0 ≤ a , b ≤ 5 a + b > 5 The solution may be feasible or infeasible. Infeasible solutions usually disobey some constraint of the problem and are hence not valid solutions.
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