Debiasing Training Data for Inductive Expert System Construction
IEEE Transactions on Knowledge and Data Engineering
Evolutionary Programming-Based Uni-vector Field Method for Fast Mobile Robot Navigation
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
Multiple Lagrange Multiplier Method for Constrained Evolutionary Optimization
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
Dual Evolutionary Optimization
Selected Papers from the 5th European Conference on Artificial Evolution
Numerical Comparison of Some Penalty-Based Constraint Handling Techniques in Genetic Algorithms
Journal of Global Optimization
Control of Underactuated Manipulators using Fuzzy Logic Based Switching Controller
Journal of Intelligent and Robotic Systems
A new adaptive penalty scheme for genetic algorithms
Information Sciences: an International Journal - Special issue: Evolutionary computation
International Journal of Systems Science
A new optimization method: big bang-big crunch
Advances in Engineering Software
Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
A line up evolutionary algorithm for solving nonlinear constrained optimization problems
Computers and Operations Research
Engineering Applications of Artificial Intelligence
A new optimization method: Big Bang-Big Crunch
Advances in Engineering Software
An adaptive penalty formulation for constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Comprehensive learning particle swarm optimization for reactive power dispatch
Applied Soft Computing
Evolutionary computation and structural design: A survey of the state-of-the-art
Computers and Structures
Particle swarm optimization driven by evolving elite group
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A novel hybrid constraint handling technique for evolutionary optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An adaptive penalty scheme for steady-state genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Performance evaluation and population reduction for a self adaptive hybrid genetic algorithm (SAHGA)
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Ensemble of constraint handling techniques
IEEE Transactions on Evolutionary Computation
A novel multi-objective PSO algorithm for constrained optimization problems
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
A closed loop algorithms based on chaos theory for global optimization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Improving a local search technique for network optimization using inexact forecasts
ICN'05 Proceedings of the 4th international conference on Networking - Volume Part I
Virtual network embedding through topology awareness and optimization
Computer Networks: The International Journal of Computer and Telecommunications Networking
Surrogate-assisted evolutionary programming for high dimensional constrained black-box optimization
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
A genetic algorithm based augmented Lagrangian method for constrained optimization
Computational Optimization and Applications
Advances in Artificial Intelligence
A penalty function-based differential evolution algorithm for constrained global optimization
Computational Optimization and Applications
| Hi-index | 0.00 |
Two evolutionary programming (EP) methods are proposed for handling nonlinear constrained optimization problems. The first, a hybrid EP, is useful when addressing heavily constrained optimization problems both in terms of computational efficiency and solution accuracy. But this method offers an exact solution only if both the mathematical form of the objective function to be minimized/maximized and its gradient are known. The second method, a two-phase EP (TPEP) removes these restrictions. The first phase uses the standard EP, while an EP formulation of the augmented Lagrangian method is employed in the second phase. Through the use of Lagrange multipliers and by gradually placing emphasis on violated constraints in the objective function whenever the best solution does not fulfill the constraints, the trial solutions are driven to the optimal point where all constraints are satisfied. Simulations indicate that the TPEP achieves an exact global solution without gradient information, with less computation time than the other optimization methods studied here, for general constrained optimization problems