Genetic algorithm (GA)
A Constructive hybrid genetic algorithm for the flowshop scheduling problem
... new Genetic Algorithm ...traditional genetic algorithm, since it has a random function that cannot give such a ...Constructive Genetic Algorithm) and it follows, therefore, all ...
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Hybrid Architecture of Genetic Algorithm and Simulated Annealing
... Abstract—This paper discusses novel dedicated hardware architecture for hybrid optimization based on Genetic algorithm (GA) and Simulated Annealing (SA). The proposed architecture achieves high speed ...
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Multiprocessor Scheduling Using Parallel Genetic Algorithm
... parallelization. Genetic algorithm (GA) has been successfully applied to solve the scheduling ...slave algorithm outperforms the sequential algorithm in case of complex and high number of ...
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Order Reduction of Linear Interval Systems Using Genetic Algorithm
... an algorithm for order reduction of higher order linear interval system into stable lower order linear interval system by means of Genetic ...this algorithm the numerator and denominator polynomials ...
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An Improved k-Nearest Neighbor Classification Using Genetic Algorithm
... The KNN classifier is one of the most popular neighborhood classifier in pattern recognition. However, it has limitations such as: great calculation complexity, fully dependent on training set, and no weight difference ...
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Optimizing Inventory Using Genetic Algorithm for Efficient Supply Chain Management
... using Genetic algorithm and the optimal product that needs to be kept in the level of control is determined on the basis of the knowledge of the past ...
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No-wait Flowshop Scheduling Using Genetic Algorithm
... Abstract— No-wait flowshop scheduling is a constrained flow shop scheduling problem that exists widely in manufacturing systems. This paper considers minimization of makespan (total completion time) for n number of jobs ...
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A Genetic Algorithm Approach for Solving a Flexible Job ShopScheduling Problem
... Flexible job shop scheduling has been noticed as an effective manufacturing system to cope with rapid development in today’s competitive environment. Flexible job shop scheduling problem (FJSSP) is known as a NP-hard ...
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Eddy current testing probe optimization using a parallel genetic algorithm
... Abstract: This paper uses the developed parallel version of Michalewicz’s Genocop III, Genetic Algorithm (GA) searching technique to optimize the coil geometry of an eddy current non-destructive testing ...
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A HYBRID OPTIMIZATION ALGORITHM BASED ON GENETIC ALGORITHM AND ANT COLONY OPTIMIZATION
... Genetic Algorithm (GA) is one of the powerful optimization methods based on the process of natural ...The algorithm will seek the shortest path from the nest to the food source by maintaining the ...
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MIMO array capacity optimization using a genetic algorithm
... a genetic algorithm to obtain the position and orientation of each MIMO array antenna that maximizes the ergodic capacity for a given propagation ...a genetic algorithm (GA)-optimized array is ...
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Input Frequencies Optimization Based on Genetic Algorithm for Maximal Mutual Information
... A genetic algorithm (GA) offers an alternative to traditional local search ...optimization algorithm inspired by the well-known biological processes of genetics and ...in genetic changes ...
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Convalesce Optimization for Input Allocation Problem Using Hybrid Genetic Algorithm
... search algorithm with some of the innovative flair of human ...solutions. Genetic algorithms were initially developed by John ...development, Genetic Algorithms have been used as optimization tools ...
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A Biased Random Key Genetic Algorithm Approach for Unit Commitment Problem
... applying Genetic Algorithms (GAs) to find good quality solutions for the UC ...proposed algorithm a solution is encoded as a vector of N real random keys in the interval [0, 1], where N is the number of ...
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A genetic algorithm to solve a multi-product distribution problem
... We will use a genetic algorithm (GA) built from scratch in Python language. The programming language chosen is, first, the one that the mentee has some knowledge of and, secondly, Python’s flexibility, ...
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Overview of Tumor Detection using Genetic Algorithm
... Original genetic algorithm assumed a generational approach: selection, recombination and mutation were applied to a population of M chromosomes until a new set of M individuals had been ...
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JOB SHOP RE- SCHEDULING USING GENETIC ALGORITHM – A CASE STUDY
... schedule. Genetic algorithms are a random search technique, invented by Holland (1975), which can find the global optimal solution by using the principles of natural genetics and natural ...selection. ...
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Function Approximation of Seawater Density Using Genetic Algorithm
... (RBNN) genetic algorithms, Particle Swarm Optimization (PSO), and Self Organizing Map ...the genetic algorithm was employed to enhance the ...blending genetic algorithm with RBNN in one ...
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Genetic Algorithm applied to Traffic Control Problems
... called genetic algorithm, in the resolution of a problem con- cerning the actual optimization of a high com- plex system focusing the aeronautical infra- structure ...of Genetic Algorithms, a field ...
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A genetic algorithm for the ligand-protein docking problem
... “steady-state” genetic algorithm (SSGA) using a grid-based methodology in docking five HIV-1 protease-ligand complexes having known three-dimensional ...implemented genetic algorithm was able ...
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