The main focus of this paper is on the family of evolutionary algorithms and their real-life
applications. We present the following algorithms: genetic algorithms, genetic programming …

Scalability of optimization algorithms is a major challenge in coping with the ever-growing
size of optimization problems in a wide range of application areas from high-dimensional …

Computationally efficient algorithms for large-scale black-box optimization have become
increasingly important in recent years due to the growing complexity of engineering and …

Genetic Algorithms are heuristic search approaches that are applicable to a wide range of
optimization problems. This flexibility makes them attractive for many optimization problems …

As a form of evolutionary computation, particle swarm optimization is less effective in large
scale optimization since it is unable to effectively balance exploration and exploitation. To …

During the last three decades, the water resources engineering field has received a
tremendous increase in the development and use of meta-heuristic algorithms like …

Evolution strategies are a class of evolutionary algorithms for black-box optimization and
achieve state-of-the-art performance on many benchmarks and real-world applications …

Learning to rank (LTR) is the process of constructing a model for ranking documents or
objects. It is useful for many applications such as Information retrieval (IR) and …

Particle swarm optimization is found ineffective in large-scale optimization. The main reason
is that particle swarlarge-scalem optimization cannot effectively balance convergence and …

Existing studies in black-box optimization suffer from low generalizability, caused by a
typically selective choice of problem instances used for training and testing of different …