Genetic Algorithms in Resource Economic Models

midra.uni-miskolc.hu

By placing biological and economical evolutions in parallel, dynamic processesnamely the processes undergoing different changes with time -can be described by genetic algorithms. The first step in looking for analogues in biological and economic processes is the application of the methodology described in the third chapter, that is placing the concepts used in both disciplines in parallel and defining the degree of analogyidentified in them. On the basis of these considerations, it can be stated that most biological concepts can be adopted to an economic and social environment, providing basis for successful modelling.

WIT Press eBooks, 2012

This chapter is dedicated to the method of genetic algorithms and its application to water resources management problems. It consists of two parts. The fi rst one includes a concise overview of the method, namely: (a) Description of its basic features, (b) Presentation of the basic genetic operators (selection, crossover, and mutation), and of some additional ones, too, (c) Outline of techniques for handling constraints and of conditions for the termination of the optimization process, and (d) Discussion on the use of surrogate fl ow models. The overview is followed by an application example to a simple groundwater management problem, namely optimal distribution of a given total fl ow rate to a number of wells. It aims at clarifying the basic notions and the use of genetic algorithms (from chromosome length to constraint handling). The second part of the chapter includes brief presentations of indicative applications of genetic algorithms to: (a) groundwater resources management (with special subsections on coastal aquifers and on groundwater remediation and pollution control), (b) the inverse problem of groundwater hydraulics, (c) surface water hydrology, (d) reservoir management, and (e) related optimization problems. These applications demonstrate the wide range of applicability, the usefulness, and the versatility of the method. The chapter ends with brief concluding remarks.

Journal of Economic Dynamics and Control, 1998

This dissertation introduces a new adaptive search algorithm, Genetic Algorithm (G A), for dynamic game applications. Since GAs require little knowledge of the problem itself, computations based on these algorithms are very attractive for opti mizing complex dynamic structures. Part one discusses GA in general, and dynamic game applications in particular. Part two is comprised of three essays on computa tional economics. In Chapter one, a genetic algorithm is developed to approximate open-loop Nash equilibria in non-linear difference games of fixed duration. Two sample problems are provided to verify the success of the algorithm. Chapter two covers discrete-time dynamic games with more than two conflicting parties. In games with more than two players, there arises the possibility of coalitions among groups of players. A three-country, two-bloc trade model analyzes the impact of coalition formation on optimal policies. Chapter three extends GA further to solve open-loop differential games of infinite duration. In a dynamic North/South trade game with transboundary knowledge spillover and local pollution optimal policies are searched. Cooperative and noncooperative modes of behavior are considered to address the welfare effects of pollution and knowledge externalities. K e y w o r d s : Genetic Algorithm, Dynamic Games, North/South Trade, Externali ties. I am thankful to Kadriye Göksel, Ismail Sağlam, Jülide Yıldırım and Murat Yülek. Their support helped to maintain my enthusiasm. Most of all, I am grateful to my family and friends who provided me with constant support during my studies.

1999

Abstract An empirical work is described which compares the optimization levels produced by a group of economic agents versus those of a similar group of economic agents which additionally employ a genetic algorithm (GA) to attain a higher level of optimization. The problem domain is multimodal. It incorporates multiple hard and soft constraints and dynamical behaviors. It also has areas of infeasibility and non-linear behaviors.

cerc.wvu.edu

We report on the application of an evolutionary algorithm in the analysis of data from experiments in economics. The algorithm was used in conjunction with regression analysis to evaluate various variable subsets and find those sets best capable of explaining the experimental outcomes. This evolutionary computation approach is offered as a supplemental method to generate optimal parsimonious subsets according to user specified criteria, or to validate subsets generated by more traditional means such as stepwise regression.

Springer Proceedings in Mathematics & Statistics, 2014

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The Journal of Artificial Societies and Social Simulation, 2003

Computational Management Science, 2009

Enterprises often implement a measurement system to monitor their march towards their strategic goals. Although this way it is possible to assess the progress of each goal, there is no structured way to reconsider resource allocation to those goals and to plan an optimal (or near optimal) allocation scheme. In this study we propose a genetic approach to match each goal with an autonomous entity (agent) with a specific resource sharing behavior. The overall performance is evaluated through a set of functions and genetic algorithms are used to eventuate in approximate optimal behavior's schemes. To outline the strategic goals of the enterprise we used the balanced scorecard method. Letting agents deploy their sharing behavior over simulation time, we measure the scorecard's performance and detect distinguished behaviors, namely recommendations for resource allocation.

Applied Mathematics and Computation, 2006

A Resource Investment Problem with Discounted Cash Flows is a project scheduling problem in which the availability levels of the resources are considered decision variables and the goal is to find a schedule and resource requirement levels such that the net present value of the project cash flows optimizes. In this paper, we present a genetic algorithm to solve this problem. We explain the elements of the algorithm such as chromosome structure, fitness function, crossover, mutation, and local improvement operations and solve more than 220 problems with known optimal solutions to evaluate the performance of the proposed algorithm. The results of the experimentation are quite satisfactory.

2017

Optimization in agriculture can have various forms, depending on its various processes or the market evolution. It can be financial or product-based and can lead to minimization, if related to costs, or maximization, related to profit. In this paper we will present a method of determining an optimal repartition of an agricultural surface for a number of cultures based on a genetic algorithm in order for the profit to be maxim. The model takes into consideration several requirements related to cultures selling price, obtained production, direct costs and costs related to human workforce. The genetic algorithm uses basic structures (chromosomes, genes) and operations (mutation, crossover) to generate a repartition. In this case, the chromosomes are defined as the surface itself, a gene within a chromosome being the surface for a specific culture. The length of the chromosome is equal to the number of cultures desired to be cultivated. The operations are basic and will be described in ...