Computational Complexity, Genetic Programming, and Implications
نویسندگان
چکیده
Recent theory work has shown that a Genetic Program (GP) used to produce programs may have output that is bounded above by the GP itself [l]. This paper presents proofs that show that 1) a program that is the output of a GP or any inductive process has complexity that can be bounded by the Kolmogorov complexity of the originating program; 2) this result does not hold if the random number generator used in the evolution is a true random source; and 3) an optimization problem being solved with a GP will have a complexity that can be bounded below by the growth rate of the minimum length problem representation used for the implementation. These results are then used to provide guidance for GP implementation.
منابع مشابه
A Mathematical Programming Model and Genetic Algorithm for a Multi-Product Single Machine Scheduling Problem with Rework Processes
In this paper, a multi-product single machine scheduling problem with the possibility of producing defected jobs, is considered. We concern rework in the scheduling environment and propose a mixed-integer programming (MIP) model for the problem. Based on the philosophy of just-in-time production, minimization of the sum of earliness and tardiness costs is taken into account as the objective fu...
متن کاملTwo-stage stochastic programming model for capacitated complete star p-hub network with different fare classes of customers
In this paper, a stochastic programming approach is applied to the airline network revenue management problem. The airline network with the arc capacitated single hub location problem based on complete–star p-hub network is considered. We try to maximize the profit of the transportation company by choosing the best hub locations and network topology, applying revenue management techniques to al...
متن کاملFlow Shop Scheduling Problem with Missing Operations: Genetic Algorithm and Tabu Search
Flow shop scheduling problem with missing operations is studied in this paper. Missing operations assumption refers to the fact that at least one job does not visit one machine in the production process. A mixed-binary integer programming model has been presented for this problem to minimize the makespan. The genetic algorithm (GA) and tabu search (TS) are used to deal with the optimization...
متن کاملGenetic and Improved Shuffled Frog Leaping Algorithms for a 2-Stage Model of a Hub Covering Location Network
Hub covering location problem, Network design, Single machine scheduling, Genetic algorithm, Shuffled frog leaping algorithm Hub location problems (HLP) are synthetic optimization problems that appears in telecommunication and transportation networks where nodes send and receive commodities (i.e., data transmissions, passengers transportation, express packages, postal deliveries, etc....
متن کاملMinimizing Stoppage Cost of an Assembly Line Using Genetic Algorithm
This paper presents a nonlinear mixed-integer programming model to minimize the stoppage cost of mixed-model assembly lines. Nowadays, most manufacturing firms employ this type of line due to the increasing varieties of products in their attempts to quickly respond to diversified customer demands. Advancement of new technologies, competitiveness, diversification of products, and large customer ...
متن کاملMinimizing Stoppage Cost of an Assembly Line Using Genetic Algorithm
This paper presents a nonlinear mixed-integer programming model to minimize the stoppage cost of mixed-model assembly lines. Nowadays, most manufacturing firms employ this type of line due to the increasing varieties of products in their attempts to quickly respond to diversified customer demands. Advancement of new technologies, competitiveness, diversification of products, and large customer ...
متن کامل