|
- EVOLUTIONARY OPTIMIZATION For searching for optimal levels of produced energy for the pollution sources, we have selected one modern heuristic method of global optimum search, namely: evolutionary computation technique. In contrast to classic methods, evolutionary computation technique of optimum search allows for incorporation of knowledge of the environment to be embedded in the algorithm structure, and enables the search to continue (no breaks) in case of environment changes. In evolutionary algorithms, optimal solutions cannot be guaranteed to be found all the time, on the other hand many reasonable suboptimal solutions could be reached. In evolutionary process a group of individuals compete with each other to survive and reproduce. Fitness of an individual controls chances of its sexual reproduction and chances of selection to the next generation of its offspring. Briefly, evolution is an iterated process of reproduction and selection. In the evolutionary algorithms, a group of solutions from the domain of the problem is represented by a group of individuals. A new group of offspring individuals is generated with the search operators, and then the next generation of solution is selected. In fact, it is the same iterated process of reproduction and selection, as in natural evolution. |
