Evolutionary algorithms (EAs) are random optimization methods inspired by genetics and
natural selection, resembling simulated annealing. We develop a method that can be used …

Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are
probabilistic algorithms for optimization, which mimic operators from natural selection and …

Performance of evolutionary algorithms in real space is evaluated by local measures such
as success probability and expected progress. In high-dimensional landscapes, most …

From a global viewpoint, evolutionary algorithms (EAs) working on continuous search-
spaces can be regarded as homogeneous Markov chains (MCs) with discrete time and …

This paper presents a data-driven optimization method based on tree search-based
reinforcement learning to solve strongly non-separable mixed-integer problems. With this …

Performance of evolutionary algorithms is evaluated by local measures such as success
probability and expected progress. In high dimensional landscapes, most algorithms rely on …

Due to the rapid increase of dimensions and complexity of real life problems, it has become
more difficult to find optimal solutions using only exact mathematical methods. The need to …

For each t= 0, 1, 2,..., let Pt be the random variable'(best individual from) EA population at
iteration t'. Then {Pt} t≥ 0 is a stochastic process on n. We also define a distance d: n←+ 0 …

Covariance Matrix Adaptation-Evolution Strategies (CMA-ES) is a renowned state-of-the-art
black-box optimzation algorithm in the field of Evolutionary Computation. As real-world …

Due to the rapid increase of dimensions and complexity of real life problems, it has become
more difficult to find optimal solutions using only exact mathematical methods. The need to …