Solving multi-objective problems with Building Blocks Identification

Multiple-objective problems are a challenge for evolutionary algorithms. The requirement to improve the quality of the solution and at the same time maintain good candidates which may have different and conflicting objectives is a difficult one. This work proposes to apply the concept of building blocks to improve evolutionary algorithms to tackle such problems. Building block identification algorithm is used to guide the crossover operator in order to maintain good building blocks and mix them effectively. The proposed method is evaluated by using building block identification guided crossover in a well-known genetic algorithm to solve multiple-objective problems. The result shows that the proposed method is effective. Moreover, it obtains a good spread of solutions even when the building blocks are loosely encoded.