Adaptive strategy in differential evolution via explicit exploitation and exploration controls

• URL: http://arxiv.org/abs/2002.00612v2
• Date: Thu, 2 Dec 2021 04:46:48 GMT
• Title: Adaptive strategy in differential evolution via explicit exploitation and exploration controls
• Authors: Sheng Xin Zhang, Wing Shing Chan, Kit Sang Tang, Shao Yong Zheng
• Abstract summary: This paper proposes a new strategy adaptation method, named explicit adaptation scheme (Ea scheme) Ea scheme separates multiple strategies and employs them on-demand. Experimental studies on benchmark functions demonstrate the effectiveness of Ea scheme.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing multi-strategy adaptive differential evolution (DE) commonly involves trials of multiple strategies and then rewards better-performing ones with more resources. However, the trials of an exploitative or explorative strategy may result in over-exploitation or over-exploration. To improve the performance, this paper proposes a new strategy adaptation method, named explicit adaptation scheme (Ea scheme), which separates multiple strategies and employs them on-demand. It is done by dividing the evolution process into several Selective-candidate with Similarity Selection (SCSS) generations and adaptive generations. In the SCSS generations, the exploitation and exploration needs are learnt by utilizing a balanced strategy. To meet these needs, in adaptive generations, two other strategies, exploitative or explorative is adaptively used. Experimental studies on benchmark functions demonstrate the effectiveness of Ea scheme when compared with its variants and other adaptation methods. Furthermore, performance comparisons with state-of-the-art evolutionary algorithms and swarm intelligence-based algorithms show that EaDE is very competitive.

Related papers

• Deep Reinforcement Learning for Online Optimal Execution Strategies [49.1574468325115]
  This paper tackles the challenge of learning non-Markovian optimal execution strategies in dynamic financial markets. We introduce a novel actor-critic algorithm based on Deep Deterministic Policy Gradient (DDPG) We show that our algorithm successfully approximates the optimal execution strategy.
  arXiv  Detail & Related papers  (2024-10-17T12:38:08Z)
• An Efficient Reconstructed Differential Evolution Variant by Some of the Current State-of-the-art Strategies for Solving Single Objective Bound Constrained Problems [5.095287502726488]
  We propose a strategy recombination and reconstruction differential evolution algorithm called reconstructed differential evolution (RDE) to solve single-objective bounded optimization problems. Based on the benchmark suite of the 2024 IEEE Congress on Evolutionary Computation, we tested RDE and several other advanced differential evolution variants.
  arXiv  Detail & Related papers  (2024-04-25T01:48:44Z)
• RLEMMO: Evolutionary Multimodal Optimization Assisted By Deep Reinforcement Learning [8.389454219309837]
  multimodal optimization problems (MMOP) requires finding all optimal solutions, which is challenging in limited function evaluations. We propose RLEMMO, a Meta-Black-Box Optimization framework, which maintains a population of solutions and incorporates a reinforcement learning agent. With a novel reward mechanism that encourages both quality and diversity, RLEMMO can be effectively trained using a policy gradient algorithm.
  arXiv  Detail & Related papers  (2024-04-12T05:02:49Z)
• Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities [63.258517066104446]
  Reinforcement learning integrated as a component in the evolutionary algorithm has demonstrated superior performance in recent years. We discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature. In the applications of RL-EA section, we also demonstrate the excellent performance of RL-EA on several benchmarks and a range of public datasets.
  arXiv  Detail & Related papers  (2023-08-25T15:06:05Z)
• REX: Rapid Exploration and eXploitation for AI Agents [103.68453326880456]
  We propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX. REX introduces an additional layer of rewards and integrates concepts similar to Upper Confidence Bound (UCB) scores, leading to more robust and efficient AI agent performance.
  arXiv  Detail & Related papers  (2023-07-18T04:26:33Z)
• Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies [50.10277748405355]
  Noise-Reuse Evolution Strategies (NRES) is a general class of unbiased online evolution strategies methods. We show NRES results in faster convergence than existing AD and ES methods in terms of wall-clock time and number of steps across a variety of applications.
  arXiv  Detail & Related papers  (2023-04-21T17:53:05Z)
• Diverse Policy Optimization for Structured Action Space [59.361076277997704]
  We propose Diverse Policy Optimization (DPO) to model the policies in structured action space as the energy-based models (EBM) A novel and powerful generative model, GFlowNet, is introduced as the efficient, diverse EBM-based policy sampler. Experiments on ATSC and Battle benchmarks demonstrate that DPO can efficiently discover surprisingly diverse policies.
  arXiv  Detail & Related papers  (2023-02-23T10:48:09Z)
• Evolutionary Action Selection for Gradient-based Policy Learning [6.282299638495976]
  Evolutionary algorithms (EAs) and Deep Reinforcement Learning (DRL) have recently been combined to integrate the advantages of the two solutions for better policy learning. We propose Evolutionary Action Selection-Twin Delayed Deep Deterministic Policy Gradient (EAS-TD3), a novel combination of EA and DRL.
  arXiv  Detail & Related papers  (2022-01-12T03:31:21Z)
• Portfolio Search and Optimization for General Strategy Game-Playing [58.896302717975445]
  We propose a new algorithm for optimization and action-selection based on the Rolling Horizon Evolutionary Algorithm. For the optimization of the agents' parameters and portfolio sets we study the use of the N-tuple Bandit Evolutionary Algorithm. An analysis of the agents' performance shows that the proposed algorithm generalizes well to all game-modes and is able to outperform other portfolio methods.
  arXiv  Detail & Related papers  (2021-04-21T09:28:28Z)
• Improved active output selection strategy for noisy environments [0.0]
  The test bench time needed for model-based calibration can be reduced with active learning methods for test design. This paper presents an improved strategy for active output selection.
  arXiv  Detail & Related papers  (2021-01-10T08:27:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.