Optimization via Parameter Mapping with Genetic Programming
نویسندگان
چکیده
This paper describes a new approach for parameter optimization that uses a novel representation for the parameters to be optimized. By using genetic programming, the new method evolves functions that transform initial random values for the parameters into optimal ones. This new representation allows the incorporation of knowledge about the problem being solved to improve the search. Moreover, the new approach addresses the scalability problem by using a representation that, in principle, is independent of the size of the problem being addressed. Promising results are reported, comparing the new method with differential evolution and particle swarm optimization on a test suite of benchmark problems.
منابع مشابه
On the optimization of Dombi non-linear programming
Dombi family of t-norms includes a parametric family of continuous strict t-norms, whose members are increasing functions of the parameter. This family of t-norms covers the whole spectrum of t-norms when the parameter is changed from zero to infinity. In this paper, we study a nonlinear optimization problem in which the constraints are defined as fuzzy relational equations (FRE) with the Dombi...
متن کاملA parameter-tuned genetic algorithm for vendor managed inventory model for a case single-vendor single-retailer with multi-product and multi-constraint
This paper develops a single-vendor single-retailer supply chain for multi-product. The proposed model is based on Vendor Managed Inventory (VMI) approach and vendor uses the retailer's data for better decision making. Number of orders and available capital are the constraints of the model. In this system, shortages are backordered; therefore, the vendor’s warehouse capacity is another limitati...
متن کاملParameter Mapping: A genetic programming approach to function optimization
This paper describes a new approach to optimization that uses a novel representation for the parameters to be optimized. By using genetic programming, the method evolves a population of functions. The purpose of such functions is to transform initial random values of the parameters into better ones. The representation is, in principle, independent of the size of the problem being addressed. Pro...
متن کاملGeometric Programming with Stochastic Parameter
Geometric programming is efficient tool for solving a variety of nonlinear optimizationproblems. Geometric programming is generalized for solving engineering design. However,Now Geometric programming is powerful tool for optimization problems where decisionvariables have exponential form.The geometric programming method has been applied with known parameters. However,the observed values of the ...
متن کاملA Hybrid Algorithm for a Two-Echelon Location- Routing Problem with Simultaneous Pickup and Delivery under Fuzzy Demand
Location-Routing Problem (LRP) emerges as one of the hybrid optimization problems in distribution networks in which, total cost of the system would be reduced significantly by simultaneous optimization of locating a set of facilities among candidate locations and routing vehicles. In this paper, a mixed integer linear programming model is presented for a two-echelon location-routing problem wit...
متن کامل