Evolutionary programming made faster

Abstract

Abstract-Evolutionary programming (EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. EP has rather slow convergence rates, however, on some function optimization problems. In this paper, a "fast EP" (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator. The relationship between FEP and classical EP (CEP) is similar to that between fast simulated annealing and the classical version. Both analytical and empirical studies have been carried out to evaluate the performance of FEP and CEP for different function optimization problems. This paper shows that FEP is very good at search in a large neighborhood while CEP is better at search in a small local neighborhood. For a suite of 23 benchmark problems, FEP performs much better than CEP for multimodal functions with many local minima while being comparable to CEP in performance for unimodal and multimodal functions with only a few local minima. This paper also shows the relationship between the search step size and the probability of finding a global optimum and thus explains why FEP performs better than CEP on some functions but not on others. In addition, the importance of the neighborhood size and its relationship to the probability of finding a near-optimum is investigated. Based on these analyses, an improved FEP (IFEP) is proposed and tested empirically. This technique mixes different search operators (mutations). The experimental results show that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested.Index Terms-Cauchy mutations, evolutionary programming, mixing operators.

Reactive power planning with voltage stability enhancement using covariance matrix adapted evolution strategy

“…The obtained results are also compared with the results of the SQP (using MATLAB optimization toolbox). The results obtained by BFGS [9], EP [9], and improved fast EP (IFEP) [22] method are also given for IEEE-30 bus system for comparison. To verify the optimality, KKT conditions are applied to the optimal solution obtained using CMAES algorithm.…”

Multiple Chaos Embedded Gravitational Search Algorithm

“…These benchmark functions are taken from [35], [36]. F1-F23 are the most commonly used benchmark numerical functions [35], where F1 and F5 are unimodal functions; F6 is a step function which has only one minimum and is discontinuous; F7 is a noisy quartic function; F8-F13 are multimodal functions with plenty of local minima and the number of the local minima in these functions increase exponentially with the dimension of the function; F14-F23 are low dimensional functions which only have a few local minima. These functions can successfully test the searching capacity of algorithms in terms of convergence speed and global exploration ability.…”

Diversity-Based Dual-Population Genetic Algorithm (DPGA): A Review

“…The other population diversity-based works are improved fast EP (IFEP) [7], adaptive EP with Lévy mutation (ALEP) [8], island model GA (IMGA) [9], restricted truncation selection (RTS) [10], real-coded memetic algorithm (RCMA) with crossover hill climbing (XHC) [11], comprehensive learning particle swarm optimizer (CLPSO) [12], RCMA with adaptive local search (LSRCMA) [13], differential evolution with neighborhood search (NSDE) [14], covariance matrix adaptation evolution strategy (CMAES) [15], diversity-guided Candidates O M for main population Candidates O R for reserve population evolutionary programming (DGEP) [16], dynamic differential factor and population diversity [17], constrained multi-objective optimization algorithm with diversity enhanced differential evolution [18], and a particle swarm optimization with diversity-guided convergence acceleration and stagnation avoidance [19]. Park and Ruy [3] proposed novel evolutionary algorithm named DPGA.…”