This paper presents a system for accessing the potential of Genetic Programming [Koza 1992] by exploiting the availability of computer networks to distribute the computational load over a large number of machines. The goal is to utilise the CPU and memory resources of Intranet systems, to facilitate GP operations. In addition the use of a large-scale parallel network of machines allows a demeti...

The purpose of this paper is to present how the fuzzy goal programming (FGP) in the area of mathematical programming (MP) can be efficiently used for modeling and solving agricultural planning problems to achieve the target levels of production of various crops cultivated in different seasons in a year and make the profit by allocating the cultivable land properly and utilising the available pr...

due to the heterogeneous nature of granular soils and the involvement of many effective parameters in the geotechnical behavior of soil-foundation systems, the accurate prediction of shallow foundation settlements on cohesionless soils is a complex engineering problem. in this study, three new evolutionary-based techniques, including evolutionary polynomial regression (epr), classical genetic p...

Highly dynamic programming environments for embedded real-time systems require a strict isolation of real-time threads from each other to achieve dependable systems. We propose a new real-time scheduling technique, called guaranteed percentage (GP) scheme that assigns each thread a specific percentage of the processor power. A hardware scheduler in conjunction with a multithreaded processor gua...

In the field of Genetic Programming (GP), there has been a growing interest in the effects of loss of genetic diversity, which causes the whole population prematurely converge to local optima. Improving diversity of the population is always an implicit goal of almost any basic genetic programming system. Most research in this area suggests a diversity measurement and controls this quantitative ...

Since Markowitz (1952) formulated the portfolio selection problem, many researchers have developed models aggregating simultaneously several conflicting attributes such as: the return on investment, risk and liquidity. The portfolio manager generally seeks the best combination of stocks/assets that meets his/ her investment objectives. The Goal Programming (GP) model is widely applied to financ...

This article presents goal programming (GP) procedure for solving Interval-valued multilevel programming (MLP) problems by using genetic algorithm (GA) in a hierarchical decision making and planning situation of an organization. In the proposed approach, first the individual best and least solutions of the objectives of the decision makers (DMs) located at different hierarchical levels are dete...

Genetic Programming(GP) is a technique that can be used to automatically program computers to perform some required task. The technique is a kind of genetic algorithm in which the representation is a program parse tree instead of a bit-string and the fitness of each parse trees is evaluated by executing the computer program that it represents. The subject of this thesis is to investigate the us...

The goal of this assignment set is for you to become familiarized with (I) unambigously formulating complex problems in terms of optimization, (II) implementing an Evolutionary Algorithm (EA) of the Coevolutionary and Genetic Programming (GP) persuasions, (III) conducting scientific experiments involving EAs, (IV) statistically analyzing experimental results from stochastic algorithms, and (V) ...

this study provides an alternative approach to decisionmaking and its application in selecting the best attack helicopter. in thisway, principal component analysis (pca) is used to find the weights ofthe criteria after identifying the effective criteria for the evaluation andselection of attack helicopters. these weights, then, applied in theobjective function of goal programming (gp) model. fi...