Chromosomes can be evolved and new ones are generated by some genetic operations of mutation, transposition and recombination guided by a fitness function.
Evolution is a field where data are constantly argued over, but it appears that Homo sapiens has evolved with unprecedented speed over the last 8,000 to 30,000 years, which seems to many scientists, to be too fast for the operation of random mutation and natural selection alone, especially since they take millions of years to create major alterations in a species.
However, the principle underlying all these algorithms is the same that tries to find the optimal solutions by the operations of reproduction, mutation, recombination, and natural selection on a population of candidate solutions.
The evolution is performed in an iterative manner where in each step the fitness of chromosomes is evaluated and the population is altered by the operations of crossover, mutation and selection.
The VNS strategy is implemented after the genetic operation of crossover and mutation to improve the exploitation ability of the algorithm.
If so, go to Step 5; otherwise, proceed to Step 4. Step 4: Execute the GA operation of crossover and mutation to update GEP candidates; and then return to the Step 3. Step 5: Randomly generate initial integrated planning candidates, which include the decision variable X ti and n ti−j, based on the GEP candidates obtained through the above steps and (8).
The new mutation operator is based on the four kinds of mutation operation in PGA, which eliminates the mutation probability.
The purpose of mutation operation is to produce new individuals and expand the searching scope.
To avoid premature convergence, HAO algorithm is modified in this paper with the introduction of mutation operation.
One is a PGA with roulette selection and elitist selection in which four new kinds of mutation operation are proposed.
