Reinforcement learning is about learning agent models that make the best sequential
decisions in unknown environments. In an unknown environment, the agent needs to …

Many machine learning tasks involve solving complex optimization problems, such as
working on non-differentiable, non-continuous, and non-unique objective functions; in some …

Motivated by concepts in quantum mechanics and particle swarm optimization (PSO),
quantum-behaved particle swarm optimization (QPSO) was proposed as a variant of PSO …

Although the particle swarm optimisation (PSO) algorithm requires relatively few parameters
and is computationally simple and easy to implement, it is not a globally convergent …

Single-objective to multi-objective/many-objective optimization (SMO) is a new paradigm in
the evolutionary transfer optimization (ETO), since there are only “1+ 4” pioneering works on …

Particle swarm optimization (PSO), a population-based stochastic optimization algorithm,
has been successfully used to solve many complicated optimization problems. However …

Recombination (or called crossover) operators are a kind of characterizing feature of
evolutionary algorithms (EAs). The usefulness of recombination operators has been verified …

Differential evolution (DE) is one of evolutionary algorithms to effectively handle optimization
problems. We propose a population interaction network (PIN) to investigate the relationship …

Despite the apparent improvement in air quality in recent years through a series of effective
measures, the concentration of PM2. 5 and O3 in Chengdu city remains high. And both the …

Evolutionary algorithms (EAs) are heuristic algorithms inspired by natural evolution. They
are often used to obtain satisficing solutions in practice. In this paper, we investigate a …