Efficient and Accurate Parallel Genetic Algorithms

By Erick Cantú-Paz. Genetic Algorithms and Evolutionary Computation, 2000

913 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/27a38864b781bdad208bc93b5397e18805a32412

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In Artificial Intelligence

Citations in the corpus

An introduction and survey of estimation of distribution algorithms (2011, methodology): “The most common approach to parallelization in EDAs and other metaheuristics is to parallelize ﬁtness evaluation (Cant´u-Paz, 2000).”
Evolutionary Algorithms for Solving Multi-Objective Problems (2002, background): “Interested readers are referred elsewhere for further discussion of these paradigms, although in a serial sense (Cantu-Paz, 2000).”
Mathematical model and parallel genetic algorithm for hybrid flexible flowshop lot streaming problem (2012, background): “There are several known connection topologies in literature (see, for example, [9]), such as ring, mesh, fully connected, randomly connected topologies, and their variations.”
Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. (2001, methodology): “We are currently exploring the use of parallel genetic algorithms (Cantú-Paz 2000) as a robust ma-”
An enhanced genetic algorithm for structural topology optimization (2006, methodology): “Anyway, the present GA needs larger amount of CPU time to reach the convergence due to its independency of the initialization and parallel implementations of the GAs, though not implemented in the present study, can reduce the computational time significantly in general [25,42,45].”
A review of particle swarm optimization. Part I: background and development (2007, background): “The iterated evaluation of the objective function may be computationally expensive, hence the interest in computational distribution, for which evolutionary algorithms are particularly well suited, see, for example, Cantú-Paz (2000).”
Genetic algorithm approaches for the phylogenetic analysis of large biological sequence datasets under the maximum likelihood criterion (2006, background): “Such approaches have been well studied in the GA literature (see Cantu-Paz, 2001), and can be very effective in increasing search efficiency by exploring a larger area of the search space and proving a diversity of solutions for recombination operators to act upon.”
Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions (2003, methodology): “When the number of combinations to be evaluated exceeds computational feasibility, machine-learning methods such as parallel genetic algorithms ( Cantu-Paz, 2000 ) must be employed as has been done for the cellular automata method (Moore and Hahn, 2002a,b).”
Distributed evolutionary algorithms and their models: A survey of the state-of-the-art (2015, background): “The speedup and efficiency of master-slave dEAs may be limited by the master’s performance and by the communication speed between master and slaves [17].”