Zobrazeno 1 - 10
of 2 884
pro vyhledávání: '"Genetic representation"'
Publikováno v:
Global Ecology and Conservation, Vol 32, Iss , Pp e01928- (2021)
How well ex situ living collections capture the genetic variation of their wild-source populations, is an efficient indicator for assessing the success of ex situ conservation. Here, we explore the origins and genetic representation of multiple speci
Externí odkaz:
https://doaj.org/article/0914ec4b93e94059b38965292a864a7d
Publikováno v:
International Journal of Applied Metaheuristic Computing. 12:1-15
In task scheduling, the job-shop scheduling problem is notorious for being a combinatorial optimization problem; it is considered among the largest class of NP-hard problems. In this paper, a parallel implementation of hybrid cellular genetic algorit
Autor:
Emre Akadal, Mehmet Hakan Satman
Publikováno v:
Alphanumeric Journal, Vol 8, Iss 1, Pp 43-58 (2020)
Volume: 8, Issue: 1 43-58
Alphanumeric Journal
Volume: 8, Issue: 1 43-58
Alphanumeric Journal
In this paper, we introduce a new encoding-decoding strategy for the floating-point genetic algorithms and we call the genetic algorithms which use this strategy Machine Coded Genetic Algorithms. We suggest applying classical crossover and mutation o
Publikováno v:
AAAI
Scopus-Elsevier
Scopus-Elsevier
Reinforcement Learning (RL) has achieved impressive performance in many complex environments due to the integration with Deep Neural Networks (DNNs). At the same time, Genetic Algorithms (GAs), often seen as a competing approach to RL, had limited su
Publikováno v:
Future Generation Computer Systems. 101:221-235
Community detection in multilayer networks such as social or information networks, due to its vast practical applications has attracted many attentions in the past years. Although some researches have been done to develop monoplex methods to multilay
Autor:
Adrian A. Hopgood
Publikováno v:
Intelligent Systems for Engineers and Scientists, Third Edition
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cad6b1debd2c17540236604bdbc223af
https://doi.org/10.1201/9781003226277-7
https://doi.org/10.1201/9781003226277-7
Autor:
Tim Grabowski, Jason Orlosky
Publikováno v:
GECCO
Neural networks with temporal characteristics such as asynchronous spiking have made much progress towards biologically plausible artificial intelligence. However, genetic approaches for evolving network structures in this area are still relatively u
Autor:
Jyh-Da Wei
Publikováno v:
Traveling Salesman Problem
Genetic algorithms (GAs) were developed as problem independent search algorithms (Goldberg, 1989; Holland, 1975; Man et al., 1999), which simulate the biological evolution to search for an optimal solution to a problem. Figure 1(a) shows the main pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::227bb449aedad9c4c3b55262677fb6de
http://www.intechopen.com/articles/show/title/approaches_to_the_travelling_salesman_problem_using_evolutionary_computing_algorithms
http://www.intechopen.com/articles/show/title/approaches_to_the_travelling_salesman_problem_using_evolutionary_computing_algorithms
Publikováno v:
Hunter, D C, Pemberton, J M, Pilkington, J G & Morrissey, M B 2019, ' Pedigree-based estimation of reproductive value ', Journal of Heredity, vol. 110, no. 4, esz033, pp. 433-444 . https://doi.org/10.1093/jhered/esz033
How successful an individual or cohort is, in terms of their genetic contribution to the future population, is encapsulated in the concept of reproductive value, and is crucial for understanding selection and evolution. Long-term studies of pedigreed
The t-spanner problem is a popular combinatorial optimization problem and has different applications in communication networks and distributed systems. This chapter considers the problem of constructing a t-spanner subgraph H in a given undirected ed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f18fa2654d685638f2407ed258d3d24b
https://doi.org/10.4018/978-1-7998-8048-6.ch019
https://doi.org/10.4018/978-1-7998-8048-6.ch019