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...

Goal programming is a technique often used in engineering design activities primarily to find a compromised solution which will simultaneously satisfy a number of design goals. In solving goal programming problems, classical methods reduce the multiple goal-attainment problem into a single objective of minimizing a weighted sum of deviations from goals. Moreover, in tackling non-linear goal pro...

The review of existing human resource allocation models for a CPA firm shows that there are major shortcomings in the previous mathematical models. First, linear programming models cannot handle multiple objective human resource allocation problems for a CPA firm. Second, goal programming or multiple objective linear programming (MOLP) cannot deal with the organizational differentiation problem...

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...

In common GP approaches, the space of genotypes (search space) is identical to the space of phenotypes (solution space). Facts and theories from molecular biology suggest the introduction of non-identical genospaces and phenospaces, and a generic genotype-phenotype mapping (GPM) which maps unconstrained genotypes into syntactically correct phenotypes. Neutral variants come into eeect due to GPM...

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) ...