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Optimization RM
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- 8. Evolution Evolution Evolution is the process by which modern organisms have descended from ancient ones Microevolution Microevolution is evolution within a single population; (a population is a group of organisms that share the same gene pool). Often this kind of evolution is looked upon as change in gene frequency within a population Evolution For evolution to occur Heredity Information needs to be passed on from one generation to the next Genetic Variation There has to be differences in the characteristics of individuals in order for change to occur Differential Reproduction Some individuals need to (get to) reproduce more than others thereby increasing the frequency of their genes in the next generation Evolution Heredity Heredity is the transfer of characteristics (or traits) from parent to offspring through genes Evolution Genetic Variation Is about variety in the population and hence presence of genetic variation improves chances of coming up with “something new” The primary mechanisms of achieving genetic variation are: Mutations Gene Flow Sexual Reproduction
- 9. Evolution Mutation It is a random change in DNA It can be beneficial, neutral or harmful to the organism Not all mutations matter to evolution Evolution Gene Flow Migration of genes from one population to another If the migrating genes did not exist previously in the incident population then such a migration adds to the gene pool Evolution Sexual Reproduction This type of producing young can introduce new gene combinations through genetic shuffling
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- 60. Genetic Algorithms (IX) Issues Generation of initial population Evaluation Reproduction operation Crossover and Mutation operations and feasibility issues Representation Genetic Algorithms Benefits to engineers as an optimization tool Problem formulation is easier Allows external procedure based declarations Can work naturally in a discrete environment
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- 72. 1. Create random initial solution γ 2. Eold=cost(γ); 3. for(temp=tempmax; temp=tempmin;temp=next_temp(temp) ) { 4. for(i=0;iimax; i++ ) { 5. succesor_func(γ); //this is a randomized function 6. Enew=cost(γ); 7. delta=Enew-Eold; 8. if(delta0) 9. if(random() = exp(-delta/K*temp); 10. undo_func(γ); //rejected bad move 11. else 12. Eold=Enew //accepted bad move 13. else 14. Eold=Enew; //always accept good moves } }
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