Jinlong Li

TL;DR: A survey of MaOEAs is reported and seven classes of many-objective evolutionary algorithms proposed are categorized into seven classes: relaxed dominance based, diversity-based, aggregation- based, indicator-Based, reference set based, preference-based and dimensionality reduction approaches.

TL;DR: Stochastic ranking-based multi-indicator Algorithm (SRA), adopts the stochastic ranking technique to balance the search biases of different indicators for many-objective optimization problems.

TL;DR: An improved version of TAA is proposed, namely ITAA, which incorporates a ranking mechanism for updating CA which enables truncating CA while CA overflows, and a shifted density estimation technique is embedded to replace the old ranking method in DA.

TL;DR: The concept of Fitness-Probability Cloud (fpc) is presented to characterise evolvability from the point of view of escape probability and fitness correlation, and a numerical measure called Accumulated Escape Probability (aep) based on fpc is proposed to quantify this feature, and therefore problem difficulty.

TL;DR: RWLS is a heuristic algorithm that has three major components united in its local search framework: a weighting scheme, which updates the weights of uncovered elements to prevent convergence to local optima, tabu strategies to avoid possible cycles during the search, and a timestamp method to break ties when prioritizing sets.