Inspired by the biological evolution, this paper proposes an evolutionary synthesis
mechanism to automatically evolve DenseNet towards high sparsity and efficiency for …

This paper carries out the performance analysis of three control system structures and
approaches, which combine Reinforcement Learning (RL) and Metaheuristic Algorithms …

Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-
level control in many sequential decision-making problems, yet many open challenges still …

Over the past two decades, to overcome the limitations of certain algorithms, ensemble
strategies or self-adaptive mechanisms for evolutionary computation algorithms have been …

We propose a new method called the Reinforced Hybrid Genetic Algorithm (RHGA) for
solving the famous NP-hard Traveling Salesman Problem (TSP). Specifically, we combine …

High dimensional datasets usually suffer from curse of dimensionality which may increase
the classification time and decrease the classification accuracy beyond a certain …

In response to the limitations of reinforcement learning and evolutionary algorithms (EAs) in
complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a …

Proven as an efficient population-based optimization algorithm, Covariance Matrix
Adaptation Evolution Strategy (CMA-ES) features two evolution paths, one to update the …

Deep reinforcement learning (DRL) has achieved notable success in continuous control
tasks. However, it faces challenges that limit its applicability to a wider array of tasks …

The development of large-scale cluster intelligence will inevitably lead to new problems of
adversarial game control. Aiming at the problem of high dimension and high dynamics in the …