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What is Genetic Algorithm?

1) Genetic algorithm(GA) usually is an element of evolutionary computing, that is a easily increasing area of artificial intelligence.

2) Searches for good solutions among possible solutions.

3) Uses evolutionary mechanisms including natural selection, reproduction, mutation.

4) It is method to solve the complicated or easy problems which need optimization.

5) The best possible solution may be missed.

6) Depend on the theory that evolution signifies search for optimum solution set.

7) Useful in problem is that are too big or too difficult to solve with conventional techniques.

8) This algorithm are generally based on evolution of Darwin's theory                             


1: g <- 0; // initialise the the value is 0
2: InitPopulation [Pop( g )]; { 
3: EvalPopulaton[Pop( g )]; { 
4: NitTermination, do
5: Pop(g) <- Variation[Pop( g )]; {
    creation of new solutions}
6: EvalPopulation[Pop( g )]; {
    assess the unique options
7: Pop(g + 1) <- apply_Genetic_Operators[Pop( g ) U Q]; {
    future era pop. }
8: g <- (g + 1):// incement with 1
9: end while

Pro Forma Cash Flow

Genetic algorithm example:

Example: find the maximum value of function.

Genetic algorithm example

- Represent problem using chomosomes built from four genes.

- Built from four genes : using chomosomes

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