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Extra resources for Introduction to Genetic Algorithms

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This idea was then developed by other researches. Genetic Algorithms (GAs) was invented by John Holland and developed this idea in his book “Adaptation in natural and artificial systems” in the year 1975. Holland proposed GA as a heuristic method based on “Survival of the fittest”. GA was discovered as a useful tool for search and optimization problems. 1 Search Space Most often one is looking for the best solution in a specific set of solutions. The space of all feasible solutions (the set of solutions among which the desired solution resides) is called search space (also state space).

2. Give a suitable example for the Genetic Algorithm principle “Survival of the fittest”. 9 Summary 3. Discuss in detail about the biological process of natural evolution. 4. Compare the terminologies of natural evolution and Genetic Algorithm. 5. Define: Search space. 6. Describe about various conventional optimization and search techniques. 7. Write short note on simple Genetic Algorithm. 8. Compare and contrast Genetic Algorithm with other optimization techniques. 9. State few advantages and disadvantages of Genetic Algorithm.

In the mid 70s, Kirlpatrick by analogy of these physical phenomena laid out the first description of simulated annealing. As in the stochastic hill climbing, the iteration of the simulated annealing consists of randomly choosing a new solution in the neighborhood of the actual solution. If the fitness function of the new solution is better than the fitness function of the current one, the new solution is accepted as the new current solution. 9) Where f (y) − f (x) is the difference of the fitness function between the new and the old solution.

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