Results 141 to 150 of about 11,497,181 (207)

An Evolutionary ILS-Perturbation Technique

2008
This contribution proposes a new perturbation technique for the iterated local search metaheuristic, which consists in a micro evolutionary algorithm that effectively explores the neighborhood of the solution that should undergo the perturbation operator.
Manuel Lozano, C. García-Martínez
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Evolutionary techniques for complex objects clustering

2017 IEEE 4th International Conference Actual Problems of Unmanned Aerial Vehicles Developments (APUAVD), 2017
The article presents the evolution technologies for clustering objects, specified by their characteristics values. The genetic algorithms and evolution strategies elements, underlying them, allow to solve clustering problems with minimal constraints on the initial data.
V. Y. Snytyuk, O. O. Suprun
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Evolutionary Techniques for EM Device synthesis

2019 IEEE Indian Conference on Antennas and Propogation (InCAP), 2019
This paper demonstrates the use of evolutionary methods for synthesis of antenna. Microstrip line fed rectangular microstrip antenna for WLAN band is the example antenna. Analysis of the synthesized antenna for 5.9 GHz uses CST microwave studio for simulation and compares its result with measurement on a prototype. A comparison of genetic algorithm (GA)
Sambhudutta Nanda, Rajanikanta Swain, Rashmirekha Kalyani Mishra, Prasanna Kumar Sahu, Rabindra Kishore Mishra   +4 more
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EVOLUTIONARY TECHNIQUE FOR DESIGNING OPTIMIZED ARRAYS

AIP Conference Proceedings, 2011
Many ultrasonic inspection applications in the industry could benefit from the use of phased array distributions specifically designed for them. Some common design requirements are: to adapt the shape of the array to that of the part to be inspected, to use large apertures for increasing lateral resolution, to find a layout of elements that avoids ...
J. Villazón, A. Ibañez, Donald O. Thompson, Dale E. Chimenti   +3 more
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A Comparison of Several Linear Genetic Programming Techniques

Complex Systems
A comparison between four Genetic Programming techniques is presented in this paper. The compared methods are Multi-Expression Programming, Gene Expression Programming, Grammatical Evolution, and Linear Genetic Programming.
Mihai Oltean, C. Grosan
semanticscholar   +1 more source

Stereo-Matching Techniques Optimisation Using Evolutionary Algorithms

2006
In this paper we present a novel approach to 3D stereo-matching which uses an evolutionary algorithm in order to optimise 3D reconstruction. Common techniques in the field of 3D models generation are employed together with a Genetic Algorithm (GA) which is able to improve the results of the matching process.
BEVILACQUA, Vitoantonio, MASTRONARDI, Giuseppe, Menolascina, F., Nitti, D.   +3 more
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Evolutionary Techniques for Speech Enhancement

2011
Genetic Algorithms have become increasingly appreciated as an easy-to-use general method for a wide range of optimization problems. Their principle consists of maintaining and manipulating a population of solutions and implementing a ‘survival of the fittest’ strategy in their search for better solutions. In this chapter, GAs are combined with a signal
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Evolutionary computation techniques and their applications

1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335), 2002
Evolutionary computation techniques, which are based on a powerful principle of evolution: survival of the fittest, constitute an interesting category of heuristic search. Evolutionary computation techniques are stochastic algorithms whose search methods model some natural phenomena: genetic inheritance and Darwinian strive for survival.
Z. Michalewicz, M. Michalewicz
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Constraint-Handling Techniques used with Evolutionary Algorithms

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2007
Evolutionary Algorithms (EAs), when used for global optimization, can be seen as unconstrained optimization techniques. Therefore, they require an additional mechanism to incorporate constraints of any kind (i.e., inequality, equality, linear, nonlinear) into their fitness function.
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Population initialization techniques for evolutionary algorithms for single-objective constrained optimization problems: Deterministic vs. stochastic techniques

Swarm and Evolutionary Computation, 2021
Alaa Tharwat, Wolfram Schenck
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