Population isolation

arithmetic crossover and optimum reserved strategy are used to improve micro-genetic algorithm (mGA). Reset frequency is decreased while the global and local searching capabilities of mGA between two resets are enhanced

which makes mGA searching the parameter space intelligently as the mode recognition information is preserved as much as possible. Realcode is used to decrease the computing cost in encoding and decoding. Adaptive random mutation with existing genetic information of the current groups is used to increase efficient search. Heterogeneous strategy is used to improve the probability of convergence to global optimal solution and quicken up the convergence. Finally

standard functions testing demonstrate that the improved mGA can find better optimum solutions with less computing cost than standard genetic algorithm (SGA).