Niching with derandomized evolution strategies in artificial and real-world landscapes

We introduce a framework of derandomized evolution strategies (ES) niching techniques. A survey of these techniques, based on 5 variants of derandomized ES, is presented, based on the fixed niche radius approach. The core mechanisms range from the very first derandomized approach to self-adaptation of ES to the sophisticated Covariance Matrix Adaptation (CMA). They are applied to artificial as well as real-world multimodal continuous landscapes, of different levels of difficulty and various dimensions, and compared with the maximum-peak-ratio (MPR) performance analysis tool. While characterizing the performance of the different derandomized variants in the context of niching, some conclusions concerning the niching formation process of the different mechanisms are drawn, and the hypothesis of a trade-off between learning time and niching acceleration is numerically confirmed. Niching with (1 + λ)-CMA core mechanism is shown to experimentally outperform all the other variants, especially on the real-world problem. Some theoretical arguments supporting the advantage of a plus-strategy for niching are discussed. For the real-world application in hand, taken from the field of Quantum Control, we show that the niching framework can overcome some degeneracy in the search space, and obtain different conceptual designs using problem-specific diversity measurements.