Comparison 3: Kita's real-coded genetic algorithm (RCGA) and SOM

Paper: Kita, E._2010_Investigation of self-organising map for genetic algorithm (pdf)

Mentioned in this bolg post Real-coded GA - opposite to binary-coded algorithms, crossover/mutation is performed on

the digits of the "normal", decimal representation of the individuals. In this particular case

it is not essential that real-coded GA is used instead of traditional. Both perform in a very

similar manner.

The conceptual diagram of RCGA-SOM for real-valued single objective function problems:

1. Apply RCGA to an initial population once to generate a new population

2. Train SOM by taking the values of an objective function and design variables of the

individuals as the input data

3. Search the best match unit on the map for each individual on the map

4. Define a sub-population by the individuals included in the circle centering the best match unit

5. Apply fitness oriented RCGA to each resulting ub-population

6. Add the fittest individual from each sub-population to the new generation Advantages: - the convergence speed is faster than that of SOM-MOEA as the local search is performed in each sub-population according to the fitness criteria - better local search performance as carries out the solution search in each sub-population instead of the solution space of the whole population.