Applying evolutionary programming to selected traveling salesman problems
Cybernetics and Systems
Evolving artificial intelligence
Evolving artificial intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
A modified strategy for the constriction factor in particle swarm optimization
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
Self-adaptive differential evolution
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Combining mutation operators in evolutionary programming
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Evolutionary programming using mutations based on the Levy probability distribution
IEEE Transactions on Evolutionary Computation
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The Classical Evolutionary Programming (CEP) relies on Gaussian mutation, whereas Fast Evolutionary Programming (FEP) selects Cauchy distribution as the primary mutation operator, Improved Fast Evolutionary (IFEP) selects the better Gaussian and Cauchy distribution as the primary mutation operator. In this paper, we propose a self-adaptive Evolutionary Programming based on Optimum Search Direction (OSDEP) in which we introduce the current best global individual into mutation to guide individuals to converge according to the global search direction. Extensive empirical studies have been carried out to evaluate the performance of OSDEP, IFEP, FEP and CEP. From the experimental results on seven widely used test functions, we can show that OSDEP outperforms all of IFEP, FEP and CEP for all the test functions.