Evolutionary Synthesis of Bayesian Networks for Optimization
Heinz Mühlenbein, Thilo Mahnig · The MIT Press eBooks · 2001
Introduction Simulating evolution as seen in nature has been identified as one of the key computing paradigms for the next decade. Today evolutionary algorithms have been successfully used in a number of applications. These include discrete and continuous optimization problems, synthesis of neural networks, synthesis of computer programs from examples (also called genetic programming) and even evolvable hardware. But in all application areas problems have been encountered where evolutionary algorithms performed badly. Therefore a mathematical theory of evolutionary algorithms is urgently needed. Theoretical research has evolved from two opposed end; from the theoretical approach there are theories emerging that are getting closer to practice; from the applied side ad hoc theories have arisen that often lack theoretical justification. Evolutionary algorithms for optimization are the easiest for theoretical analysis. Here results from classical population genetics and statistic