Lecture_1_Genetic Algorithms

The main idea about for Evolutionary Algorithm is : Given a population of individuals then using the idea of “survival of the fittest” to make natural selection and this causes arise in the fitness of the population.
Given the function to be maximized, we can randomly create a set of candidate solutions, i.e. , elements of the function’s domain , and apply the quality function and the higher is the better.
Based on this fitness some of the better candidates are chosen to seen in the next generation by applying recombination and/or mutation to them.
Recombination is an operator applied to two or more selected candidates(parents) and results one or more new candidates(Children).
Mutation is applied to one candidate and results in one new candidate.
Executing recombination and mutation leads to a set of new candidates (the offspring) that compete –based on their fitness with the old ones for a place in the next generation.
This process can be iterated until a candidate with a sufficient quality ( a solution) is found or a previously set computational limit is reached.

It is easy to see that this algorithm falls in the category of generate-and-test algorithm.
The evaluation (fitness) function represent a heuristic estimation of solution operators.
Evolutionary Algorithm posses a number of features :
EAs are population based, i.e., they process a whole collection of candidate solutions simultaneously.
EAs mostly use recombination to mix information of more candidate solutions into a new one.
EAs are Stochastic.
What are genetic algorithms?
How to design a genetic algorithm?
Genetic algorithms are a family of computational models ispired by evolution. These algorithms encode a potential solution to a specific problem on a simple chromosome-like data structure and apply recombination operators to these structures in order to preserve critical information.
Genetic Algorithm often viewed as function optimizers.