Evolutionary Computation and Genetic Algorithms

2011 ◽  
pp. 477-481
Author(s):  
William H. Hsu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Natural Selection ◽  
Genetic Programming ◽  
Sexual Reproduction ◽  
Evolutionary Computation ◽  
Evolutionary Biology ◽  
Approximate Solutions ◽  
Biologically Inspired ◽  
Genetic Inheritance

A genetic algorithm (GA) is a procedure used to find approximate solutions to search problems through the application of the principles of evolutionary biology. Genetic algorithms use biologically inspired techniques, such as genetic inheritance, natural selection, mutation, and sexual reproduction (recombination, or crossover). Along with genetic programming (GP), they are one of the main classes of genetic and evolutionary computation (GEC) methodologies.

Evolutionary Computation and Genetic Algorithms

2011 ◽  
pp. 817-822
Author(s):  
William H. Hsu
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Computer Simulation ◽  
Genetic Algorithms ◽  
Natural Selection ◽  
Evolutionary Biology ◽  
Approximate Solutions ◽  
Learning Problems ◽  
Abstract Representations ◽  
Search Optimization

A genetic algorithm (GA) is a method used to find approximate solutions to difficult search, optimization, and machine learning problems (Goldberg, 1989) by applying principles of evolutionary biology to computer science. Genetic algorithms use biologically-derived techniques such as inheritance, mutation, natural selection, and recombination. They are a particular class of evolutionary algorithms. Genetic algorithms are typically implemented as a computer simulation in which a population of abstract representations (called chromosomes) of candidate solutions (called individuals) to an optimization problem evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but different encodings are also possible. The evolution starts from a population of completely random individuals and happens in generations. In each generation, multiple individuals are stochastically selected from the current population, modified (mutated or recombined) to form a new population, which becomes current in the next iteration of the algorithm.

Heuristic Artificial Intelligent Algorithm for Genetic Algorithm

2010 ◽  
Vol 439-440 ◽  
pp. 516-521 ◽  
Author(s):  
Luo Lie
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Evolutionary Biology ◽  
Approximate Solutions ◽  
Search Technique ◽  
Intelligent Algorithm ◽  
Search Problems ◽  
Artificial Intelligent ◽  
Search Heuristics

A genetic algorithm is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.

Gene Expression Programming

2017 ◽  
pp. 269-292 ◽  
Author(s):  
Baddrud Zaman Laskar ◽  
Swanirbhar Majumder
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Natural Selection ◽  
Genetic Programming ◽  
Soft Computing ◽  
Gene Expression Programming ◽  
Research Work ◽  
Genetic Operators ◽  
A New Technique ◽  
Work Done

Gene expression programming (GEP) introduced by Candida Ferreira is a descendant of genetic algorithm (GA) and genetic programming (GP). It takes the advantage of both the optimization and search technique based on genetics and natural selection as GA and its programmatic Darwinian counterpart GP. It is gaining popularity because; it has to some extent eradicated the ‘cons' of both while keeping in the ‘pros'. It is still a new technique not much explored since its introduction in 2001. In this chapter both GA and GP is first discussed followed by the elaborate discussion of GEP. This is followed up by the discussion on research work done is different fields using GEP as a tool followed up by GEP architectures. Finally, here GEP has been used for detection of age from facial features as a soft computing based optimization problem using genetic operators.

Application of Genetic Algorithms in Inventory Control

2016 ◽  
pp. 1087-1098
Author(s):  
Vinod Kumar Mishra
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Natural Selection ◽  
Inventory Control ◽  
Future Trend ◽  
Production Engineering ◽  
Mathematical Sciences ◽  
Adaptive Heuristic ◽  
Definition Of ◽  
Key Terms

The genetic algorithm (GA) is an adaptive heuristic search procedures based on the mechanics of natural selection and natural genetics. Inventory control is widely used in the area of mathematical sciences, management sciences; system science, industrial engineering, production engineering etc. but they have wide differences in mathematical and computation maturity. This chapter enables the reader to understand the basic theory of genetic algorithm and how to apply the genetic algorithms for optimizing the parameters in inventory control The current and future trend of the research with the definition of key terms of genetic algorithm has also incorporated in this chapter.

Optimization of Multi-Modal Discrete Functions Using Genetic Algorithms

1993 ◽  
Vol 207 (1) ◽  
pp. 53-59 ◽  
Author(s):  
D T Pham ◽  
Y Yang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problem ◽  
Approximate Solutions ◽  
Solution Set ◽  
Preliminary Design ◽  
Potential Solution ◽  
Reproduction Cycle ◽  
Fitness Sharing ◽  
Discrete Functions

Four techniques are described which can help a genetic algorithm to locate multiple approximate solutions to a multi-modal optimization problem. These techniques are: fitness sharing, ‘eliminating’ identical solutions, ‘removing’ acceptable solutions from the reproduction cycle and applying heuristics to improve sub-standard solutions. Essentially, all of these techniques operate by encouraging genetic variety in the potential solution set. The preliminary design of a gearbox is presented as an example to illustrate the effectiveness of the proposed techniques.

scholarly journals Evolutionary Computation Techniques for Predicting Atmospheric Corrosion

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Amine Marref ◽  
Saleh Basalamah ◽  
Rami Al-Ghamdi
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Corrosion Rate ◽  
Genetic Programming ◽  
Atmospheric Corrosion ◽  
Structural Integrity ◽  
Good Accuracy ◽  
Residual Life ◽  
Vital Role ◽  
Engineering Structures

Corrosion occurs in many engineering structures such as bridges, pipelines, and refineries and leads to the destruction of materials in a gradual manner and thus shortening their lifespan. It is therefore crucial to assess the structural integrity of engineering structures which are approaching or exceeding their designed lifespan in order to ensure their correct functioning, for example, carrying ability and safety. An understanding of corrosion and an ability to predict corrosion rate of a material in a particular environment plays a vital role in evaluating the residual life of the material. In this paper we investigate the use of genetic programming and genetic algorithms in the derivation of corrosion-rate expressions for steel and zinc. Genetic programming is used to automatically evolve corrosion-rate expressions while a genetic algorithm is used to evolve the parameters of an already engineered corrosion-rate expression. We show that both evolutionary techniques yield corrosion-rate expressions that have good accuracy.

Gene Expression Programming

2021 ◽  
pp. 427-446
Author(s):  
Baddrud Zaman Laskar ◽  
Swanirbhar Majumder
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Natural Selection ◽  
Genetic Programming ◽  
Soft Computing ◽  
Gene Expression Programming ◽  
Research Work ◽  
Genetic Operators ◽  
A New Technique ◽  
Work Done

Gene expression programming (GEP) introduced by Candida Ferreira is a descendant of genetic algorithm (GA) and genetic programming (GP). It takes the advantage of both the optimization and search technique based on genetics and natural selection as GA and its programmatic Darwinian counterpart GP. It is gaining popularity because; it has to some extent eradicated the ‘cons' of both while keeping in the ‘pros'. It is still a new technique not much explored since its introduction in 2001. In this chapter both GA and GP is first discussed followed by the elaborate discussion of GEP. This is followed up by the discussion on research work done is different fields using GEP as a tool followed up by GEP architectures. Finally, here GEP has been used for detection of age from facial features as a soft computing based optimization problem using genetic operators.

Penjadwalan Produksi Dengan Algoritma Genetik Untuk Meniminisasi Makespan di PT AAA

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
M. Ghassan Fattah ◽  
Rosnani Ginting
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Natural Selection ◽  
Machine Scheduling ◽  
Optimal Timing ◽  
Scheduling Problems ◽  
Fitness Value ◽  
Genetic Algorithm Method

PT. AAA dari bulan Januari sampai Desember mendapat total 88 order dengan jumlah keterlambatan 12 order maka persentase keterlembatan adalah 13,63%. Tujuan penelitian ini adalah untuk merancangan penerapan algoritma genetik yang dapat menghindari keterlambatan order yaitu untuk mengukur makespan produk dan merancang urutan penjadwalan mesin. Penyelesaian masalah penjadwakan dengan algoritma genetik. Algoritma genetik merupakan teknik search stochastic yang berdasarkan mekanisme seleksi alam dan genetika natural dengan melakukan proses inisialisasi awal lalu dicari nilai fitness dari setiap individu, yang akan menjadi induk adalah yang memiliki nilai fitness terbaik lalu dilakukan proses penyilangan dan mutasi dan pemilihan waktu optimal. Dari hasil perhitungan dengan menggunakan metode Algoritma Genetika diperoleh urutan penjadwalan mesin terbaik dan dengan nilai makespan terkecil.   PT. AAA from January to December received a total of 88 orders with the number of delays of 12 orders, the percentage of bridges was 13.63%. The purpose of this study is to design the application of a genetic algorithm that can avoid delay in order to measure product makespan and design the order of machine scheduling. Resolving scheduling problems with genetic algorithms. Genetic algorithm is a search stochastic technique that is based on the mechanism of natural selection and natural genetics by carrying out the initial initialization process and then looks for the fitness value of each individual, who will be the parent who has the best fitness value and then the process of crossing and mutation and optimal timing. From the results of calculations using the Genetic Algorithm method, the best sequence of machine scheduling is obtained and with the smallest makespan value.

Application of Genetic Algorithm for the Support Location Optimization of Beams

1994 ◽  
Author(s):  
Bo Ping Wang ◽  
Jahau Lewis Chen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Natural Selection ◽  
Optimal Design ◽  
Design Process ◽  
Evolutionary Process ◽  
Location Optimization ◽  
Adaptive Procedures ◽  
Rigid Supports ◽  
Selection Of

Abstract Genetic algorithms are adaptive procedures that find solutions to problems by an evolutionary process that mimics natural selection. In this paper, the use of genetic algorithms for the selection of optimal support locations of beams is presented. Both elastic and rigid supports are considered. The approach of adapting the genetic algorithms into the optimal design process is described This approach is used to optimize locations of three supports for beam with three types of boundary conditions.

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