this research proposes a methodology for ranking decision making units byusing a goal programming model.we suggest a two phases procedure. in phase1, by using some dea problems for each pair of units, we construct a pairwisecomparison matrix. then this matrix is utilized to rank the units via the goalprogramming model.

Geometric programming (GP) provides a power tool for solving a variety of optimization problems. In the real world, many applications of geometric programming (GP) are engineering design problems in which some of the problem parameters are estimating of actual values. This paper develops a solution procedure to solve nonlinear programming problems using GP technique by splitting the cost coeffi...

The need to increase agricultural production has become a challenging task for most countries. Generally, many resource factors affect the deterioration of level, such as low water desertification, soil salinity, on capital, lack equipment, impact export and import crops, fertilizers, pesticide, ineffective role extension services which are significant in this sector. main objective research is...

Easy missions is an approach to machine learning that seeks to synthesize solutions for complex tasks from those for simpler ones. ISLES (Incrementally Staged Learning from Easier Subtasks) [1] is a genetic programming (GP) technique that achieves this by using identified goals and fitness functions for subproblems of the overall problem. Solutions evolved for these subproblems are then reused ...

Genetic programming (GP) is an evolutionary approach that extends genetic algorithms to allow the exploration of the space of computer programs. Like other evolutionary algorithms, GP works by defining a goal in the form of a quality criterion (or fitness) and then using this criterion to evolve a set (or population) of candidate solutions (individuals) by mimicking the basic principles of Darw...

This study investigates the usefulness and ef®cacy of a multiobjective decision method for ®nancial trading guided by a set of seemingly diverse analysts' forecasts. The paper proposes a goal programming (GP) approach which combines various forecasts based on the performance of their previous investment returns. In our experiment, several series of ®nancial analysts' forecasts are generated by ...

This paper investigates the role of syntactic locality and semantic locality of crossover in Genetic Programming (GP). First we propose a novel crossover using syntactic locality, Syntactic Similarity based Crossover (SySC). We test this crossover on a number of real-valued symbolic regression problems. A comparison is undertaken with Standard Crossover (SC), and a recently proposed crossover f...

abstract— in the present study, genetic programming (gp) as a completely different approach in comparison with conventional methods, based on the imitation of natural evolution of living organism is proposed for the prediction of cellulose acetate (ca) polymeric membrane characteristics. the membrane preparation parameters of polymer concentration, additive concentration and coagulation bath te...

Program induction generates a computer program that can produce the desired behavior for a given set of situations. Two of the approaches in program induction are inductive logic programming (ILP) and genetic programming (GP). Since their formalisms are so different, these two approaches cannot be integrated easily, although they share many common goals and functionalities. A unification will g...

Even though the Genetic Programming (GP) mechanism is capable of evolving any computable function, the means through which it does so is inherently flawed: the user must provide the GP engine with an evolutionary pathway toward a solution. Hence Genetic Programming is problematic as a mechanism for generating creative solutions to specific prob-