Results 251 to 260 of about 48,260 (274)

Evolutionary Computation and Meta-heuristics

2020
This chapter presents several methods of evolutionary computation and meta-heuristics. Evolutionary computation is a computation technique that mimics the evolutionary mechanism of life to select, deform, and convolute data structures. Because of its high versatility, its applications are found in various fields.
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Designing Parallel Meta-Heuristic Methods

2014
Meta-heuristics represent powerful tools for addressing hard combinatorial optimization problems. However, real life instances usually cannot be treated efficiently in “reasonable” computing times. Moreover, a major issue in meta-heuristic design and calibration is to provide high performance solutions for a variety of problems.
Teodor Gabriel Crainic, Tatjana Davidović, Dušan Ramljak   +2 more
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Meta heuristics for prime factorization problem

2013 World Congress on Nature and Biologically Inspired Computing, 2013
Generally, cryptographic algorithms are based on NP-Complete problems like prime factorization, discrete logarithm etc. The difficulties of RSA and Rabin cryptographic algorithms are based on prime factorization problem. Prime factorization problem may be modeled as a Non-uniform discrete optimization problem.
Pranav Dass, Harish Sharma, Jagdish Chand Bansal, Kendall E. Nygard   +3 more
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Meta–heuristics for Portfolio Optimization: Part I — Review of Meta–heuristics

2023
Kyle Erwin, Andries Engelbrecht
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Meta-heuristic Stochastic Optimization

2022
Tome Eftimov, Peter Korošec
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Parking recommendation with meta-heuristic algorithms

Theoretical and Natural Science
Due to the exponential growth of cars in urban areas, parking problems have become a significant concern. Addressing this issue requires efficient methods for locating available parking spaces, enhancing the overall experience for drivers. This paper introduces a parking lot recommendation model leveraging meta-heuristic algorithms to generate a list ...
Siyuan Wu, Tingting Yang
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Local search and meta-heuristic algorithms

2020
Suboptimal schedules for intractable time-dependent scheduling problems may be found by using various algorithms. In this chapter, closing the fifth part of the book, we consider local search and meta-heuristic algorithms.
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Meta-Heuristics Theory and Applications.

The Journal of the Operational Research Society, 1997
L. Proll, I. H. Osman, J. P. Kelly
openaire   +2 more sources

Genetic Algorithm and Other Meta-Heuristics

2005
Genetic algorithms have been applied in solving various types of large-scale, NP-hard optimization problems. Many researchers have been investigating its global convergence properties using Schema Theory, Markov Chain, etc. A more realistic approach, however, is to estimate the probability of success in finding the global optimal solution within a ...
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Multipoint Search Meta-Heuristics

The Proceedings of Design & Systems Conference, 2003
Keiichiro Yasuda, Takaaki Nagaoka
openaire   +1 more source