A generic framework for constrained optimization using genetic algorithms

By Sangameswar Venkatraman, Gary G. Yen. IEEE Transactions on Evolutionary Computation, 2005

338 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/2f4a45600801d855e229f9ee72f14c5f229392bf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

In Computer Networks and Communications

Citations in the corpus

Constraint Handling in Multiobjective Evolutionary Optimization (2009, methodology): “In [24], a two-phase algorithm that is based on multiobjective optimization technique is proposed.”
Differential evolution with dynamic stochastic selection for constrained optimization (2008, methodology): “Although the feasible solutions could be located quickly, the deficiency will occur in problems with disconnected feasible components, because in such cases the genetic algorithm (GA) may be stuck within one of the feasible components and never get to explore [ 16 ].”
Constrained optimization by the ε constrained differential evolution with an archive and gradient-based mutation (2010, methodology): “Venkatraman and Yen [21] proposed a two-step optimization method, which first optimized constraint violation and then objective function.”
A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization (2006, methodology): “algorithm and other methods is that, in our approach guiding the population toward feasibility and refining the fitness of individuals are pursued simultaneously by combining the operators 1 and 2, while some other methods (such as [16], [38], and [39])”
Fundamentals of natural computing: an overview (2007, background): “, [13,94,273]); investigations into evolutionary robotics, evolutionary hardware and evolutionary electronics [175,205,249,286]; biological applications and bioinformatics [17,103]; applications in data mining, games, arts and music [55,173,283]; and many others.”
Ensemble of Constraint Handling Techniques (2010, methodology): “A generic framework using genetic algorithms (AGFCOGA) [26] for solving the constrained optimization problem also uses a similar approach, but in Phase 1, the objective function is completely disregarded and the problem is treated as a constraint satisfaction problem.”
Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites (2006, methodology): “3) The constraint violation and the objective function are used separately and are optimized separately [7]–[10].”
Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, methodology): “Following the same scheme adopted by Hinterding and Michalewicz [43], Venkatraman and Yen [51] solved a CNOP in two-steps: The first one focused only on satisfying the constraints of the problem, i.”
Fuzzy-Based Pareto Optimality for Many-Objective Evolutionary Algorithms (2014, background): “Continuing research will be extended to other state-of-the-art MOEAs and to MOEAs designed specifically to handle constrained MaOPs [29], [30] and dynamic MaOPs [31], [32].”
An Efficient Codification to Solve Distribution Network Reconfiguration for Loss Reduction Problem (2008, methodology): “This technique represents an improved, more efficient proposal compared with traditional genetic algorithms according to recent research [20], [21].”