Explicit Exploration Strategy for Evolutionary Algorithms
Ackley Function for Benchmarking Optimization Algorithms
Cigar Tablet function for optimization problems
Cigar function for optimization problems
Distribution estimation algorithm with empirical cumulative distributi...
Distribution estimation algorithm with histograms.
Distribution estimation algorithm with multivariate normal distributio...
Distribution estimation algorithm with normal distribution.
Ellipsoid function for optimization problems
Expanded Schaffer's F6 Function for Benchmarking Optimization Algorith...
Explicit exploration in evolutionary algorithms
Griewank Function for Benchmarking Optimization Algorithms
Happy Cat Function for Benchmarking Optimization Algorithms
Modified Schwefel Function for Benchmarking Optimization Algorithms
Rastrigin function for optimization problems
Rosenbrock function for optimization problems
Schwefel 1.2 function for optimization problems
Sphere Function for Benchmarking Optimization Algorithms
Trid function for optimization problems
Two Axes function for optimization problems
Weierstrass Function for Benchmarking Optimization Algorithms
Zakharov function for optimization problems
Implements an explicit exploration strategy for evolutionary algorithms in order to have a more effective search in solving optimization problems. Along with this exploration search strategy, a set of four different Estimation of Distribution Algorithms (EDAs) are also implemented for solving optimization problems in continuous domains. The implemented explicit exploration strategy in this package is described in Salinas-Gutiérrez and Muñoz Zavala (2023) <doi:10.1016/j.asoc.2023.110230>.