One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate,...
Parameter setting currently ranks among the most actively researched topics in the evolutionary algorithm (EA) community. This can be explained by the major impact EA parameters have on search performance. However, parameter setting has been shown to be both problem dependent and evolution dependent. ...
摘要: Discusses methods proposed for handling nonlinear programming constraints by evolutionary algorithms for numerical optimization problems. Difficulties connected with solving general nonlinear programming problem; Approaches that have emerged in the evolutionary computation community....
2.3 Multi-objective optimization and evolutionary algorithms In traditional optimization, the goal is to find a single optimal solution that maximizes or minimizes a specific objective function. In many real-world scenarios, decision-makers face situations where there are multiple, often conflicting, obje...
Inference of sequence homology is inherently an evolutionary question, dependent upon evolutionary divergence. However, the insertion and deletion penalties in the most widely used methods for inferring homology by sequence alignment, including BLAST and profile hidden Markov models (profile HMMs), are no...
Differential evolution (DE), proposed by Storn and Price [1], [2] in 1995, has become a hotspot in the community of evolutionary computation. Similar to other evolutionary algorithms (EAs), DE is a population-based optimization algorithm. In DE, each individual in the population is called a...
We consider grid search (GS) as a representative of manual tuning and particle swarm optimization (PSO) as a representative of evolutionary algorithms. Fig. 9(e)-(h) demonstrate the optimization of DCNN models using these different methods. Despite varying approaches to hyperparameter optimization,...
IEEE Abstract—This paper proposes two intelligent evolutionary algorithms IEA and IMOEA using a novel intelligent gene col- lector (IGC) to solve single and multiobjective large parameter optimization problems, respectively. IGC is the main phase in an intelligent recombination operator of IEA and IM...
In: Torra, V., Narukawa, Y. (eds.) MDAI 2004. LNCS (LNAI), vol. 3131, pp. 92–103. Springer, Heidelberg (2004) Han, K.H., Kim, J.H.: On Setting the Parameters of Quantum-Inspired Evolutionary Algorithms for Practical Applications. In: Proc. of the 2003 Congress on ...
G. Parameterization of NDDO Wavefunctions Using Genetic Algorithms. An Evolutionary Approach to Parameterizing Potential Energy Surfaces and Direct Dynamics Calculations for Organic Reactions. Chem. Phys. Lett. 1995, 233, 231- 236.Rossi, I.; Truhlar, D. G. Parameterization of NDDO wavefunctions ...