Age-Fitness Pareto Optimization [chapter]

Michael Schmidt, Hod Lipson
2010 Genetic Programming Theory and Practice VIII  
We propose a multi-objective method for avoiding premature convergence in evolutionary algorithms, and demonstrate a threefold performance improvement over comparable methods. Previous research has shown that partitioning an evolving population into age groups can greatly improve the ability to identify global optima and avoid converging to local optima. Here, we propose that treating age as an explicit optimization criterion can increase performance even further, with fewer algorithm
more » ... tion parameters. The proposed method evolves a population on the two-dimensional Pareto front comprising (a) how long the genotype has been in the population (age); and (b) its performance (fitness). We compare this approach with previous approaches on the Symbolic Regression problem, sweeping the problem difficulty over a range of solution complexities and number of variables. Our results indicate that the multi-objective approach identifies the exact target solution more often that the age-layered population and standard population methods. The multi-objective method also performs better on higher complexity problems and higher dimensional datasetsfinding global optima with less computational effort.
doi:10.1007/978-1-4419-7747-2_8 fatcat:gl3ld2ync5fe3basoemp6fpswi