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Balanced crossover operators in Genetic Algorithms [PDF]
Swarm and Evolutionary Computation, 2020 In several combinatorial optimization problems arising in cryptography and design theory, the admissible solutions must often satisfy a balancedness constraint, such as being represented by bitstrings with a fixed number of ones. For this reason, several works in the literature tackling these optimization problems with Genetic Algorithms (GA ...
Luca Manzoni, Luca Mariot, Eva Tuba
exaly +6 more sources
Representation Invariant Genetic Operators [PDF]
Evolutionary Computation, 2010 A genetic algorithm is invariant with respect to a set of representations if it runs the same no matter which of the representations is used. We formalize this concept mathematically, showing that the representations generate a group that acts upon the search space. Invariant genetic operators are those that commute with this group action.
Jonathan E. Rowe +2 more
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Operator and parameter adaptation in genetic algorithms [PDF]
Soft Computing, 1997 Genetic Algorithms are a class of powerful, robust search techniques based on genetic inheritance and the Darwinian metaphor of “Natural Selection”. These algorithms maintain a finite memory of individual points on the search landscape known as the “population”.
Jim Smith 0002, Terence C. Fogarty
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Genetic Operators and Sequencing in the GAuGE System [PDF]
2006 IEEE International Conference on Evolutionary Computation, 2006 This paper investigates the effects of the mapping process employed by the GAuGE system on standard genetic operators. It is shown that the application of that mapping process transforms these operators into suitable sequencing searching tools. A practical application is analysed, and its results compared with a standard genetic algorithm, using the ...
Miguel Nicolau, Conor Ryan
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CROSSOVER OPERATORS IN GENETIC ALGORITHMS: A REVIEW [PDF]
ICTACT Journal on Soft Computing, 2015 The performance of Genetic Algorithm (GA) depends on various operators. Crossover operator is one of them. Crossover operators are mainly classified as application dependent crossover operators and application independent crossover operators.
A.J. Umbarkar, P.D. Sheth
doaj +2 more sources
A comparative study of adaptive mutation operators for metaheuristics [PDF]
, 2009 Genetic algorithms (GAs) are a class of stochastic optimization methods inspired by the principles of natural evolution. Adaptation of strategy parameters and genetic operators has become an important and promising research area in GAs.
Yang, S +5 more
core +9 more sources
Hybrid Multi-population Genetic Algorithm for Multi Criteria Project Selection [PDF]
Journal of Mahani Mathematical Research, 2022 Resources scarcity, available capabilities and cost-benefit point of view, make it essential to select the best project(s) from available project portfolio. Project selection process has a significant role in the success.
Mohammad Mirabi, Hossein Ghaneai
doaj +1 more source
Investigating new structures from genetic algorithm in optimization Exploitation of tanks [PDF]
Fanāvarī-i āmūzish, 2007 By building large dams in different countries of the world, increasing the efficiency and effectiveness of these reservoir systems and maximizing the benefits of them is one of the most important issues studied in recent years.
A. Afshar, M. Shafie, O. Bozorg hadad
doaj +3 more sources
A Novel Sparrow Search Algorithm for the Traveling Salesman Problem
IEEE Access, 2021 The sparrow search algorithm (SSA) tends to fall into local optima and to have insufficient stagnation when applied to the traveling salesman problem (TSP). To address this issue, we propose a novel greedy genetic sparrow search algorithm based on a sine
Changyou Wu +3 more
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IEEE Access, 2022 Neighborhood rough set is an excellent mathematical tool to carry out feature selection on both numerical and categorical dataset. However, conventional feature selection algorithms usually use greedy heuristic search strategies, which is easy to trap in
Jindong Feng, Zengtai Gong
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