Network planning and design by Slyke R.

By Slyke R.

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A genetic algorithm starts with many initial guesses and works in a parallel manner to find a list of solutions. The SA algorithm slowly becomes less random as it converges, while the GA may or may not become less random with time. Finally, the GA is more adept at working with continuous, discrete, and integer variables, or a mix of those variables. 5 GENETIC ALGORITHM The rest of this book is devoted to the relatively new optimization technique called the genetic algorithm (GA). GAs were introduced by Holland [31] and were applied to many practical problems by Goldberg [32].

That’s just slightly better than a one-in-five chance of finding the true minimum. Not very encouraging, especially when the number of dimensions increases. 21. 20. Movements of the simplex in the Nelder–Mead downhill simplex algorithm when finding the minimum of AF3. 21. Starting points for the Nelder–Mead algorithm denoted by small squares converge to the global minimum. All other starting points converge to a local minimum. 3 21 COMPARING LOCAL NUMERICAL OPTIMIZATION ALGORITHMS This section compares four local optimization approaches against a more formidable problem.

The costs must be normalized in order to develop the probabilities. The normalization is arbitrary. As a result, we recommend rank order selection over a cost-based approach. Once the probability of selection is assigned to each parent, then a uniform random number (r) is generated. 4. Tournament selection. 5678, then the second chromosome is selected. 2 Tournament Selection A second approach to finding parents randomly selects two small groups of chromosomes from the mating pool (usually two or three per group).

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