Evolutionary programming and genetic algorithms. Second Edition, Revised and Added

Evolutive programming and genetic algorithms is intended for the use of second year students and students of the doctoral school of the Bucharest Academy of Economic Studies, Faculty of Cybernetics, Statistics and Informatics for Economy (Economy Informatics section), but it is also useful for any person interested in studying the this subject.

The volume is structured in five chapters and bibliography, as follows: introductory notions, which include general concepts upon evolutive computation relies on; evolutive algorithm classes, which are based on the natural evolution principle; genetic algorithms (GA – Genetic Algorithm), the most widely used class of evolutive algorithms; GA applications for portfolio optimization problems; and an introduction to MatLab, the tool used to develop the examples in this book and in class.

The book presents evolutive solutions for some classic problems (One-Max, N queens) and economy problems (traveling salesman problem, job shop scheduling problem, stock portfolio optimization problems: primary risk minimization problem, primary return maximization problem and variants using mixed goal functions). For these problems there are comparative analyses regarding variation and selection operators, population models, survival models (next generation selection), with suggestions for convenient choices for parameter values.