Abstract-SHADE is an adaptive DE which incorporates success-history based parameter adaptation and one of the state-of-the-art DE algorithms. This paper proposes L-SHADE, which further extends SHADE with Linear Population Size Reduction (LPSR), which continually decreases the population size according to a linear function. We evaluated the performance of L-SHADE on CEC2014 benchmarks and compared its search performance with state-of-the-art DE algorithms, as well as the state-of-the-art restart CMA-ES variants. The experimental results show that L-SHADE is quite competitive with state-ofthe-art evolutionary algorithms.
One of such techniques named Linear Population Size Reduction (LPSR) was incorporated by authors in REF to enhance the performance of SHADE.
Improving the search performance of SHADE using linear population size reduction
2014 IEEE Congress on Evolutionary Computation (CEC)
2014 IEEE Congress on Evolutionary Computation (CEC)
Computer Science