An Ensemble of Evolutionary Algorithms With Both Crisscross Search and Sparrow Search for Processing Inferior Individuals

• URL: http://arxiv.org/abs/2601.10263v1
• Date: Thu, 15 Jan 2026 10:36:08 GMT
• Title: An Ensemble of Evolutionary Algorithms With Both Crisscross Search and Sparrow Search for Processing Inferior Individuals
• Abstract summary: We introduce two types of evolutionary algorithm proposed recently - crisscross search and sparrow search - into EA4eig as secondary evolutionary algorithms to process inferior individuals.<n> Experimental results show that our EA4eigCS outperforms EA4eig and is competitive when compared with state-of-the-art algorithms.
• Score: 13.79887031590359
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the field of artificial intelligence, real parameter single objective optimization is an important direction. Both the Differential Evolution (DE) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) demonstrate good performance for real parameter single objective optimization. Nevertheless, there exist other types of evolutionary algorithm for the purpose. In recent years, researchers begin to study long-term search. EA4eig - an ensemble of three DE variants and CMA-ES - performs well for long-term search. In this paper, we introduce two types of evolutionary algorithm proposed recently - crisscross search and sparrow search - into EA4eig as secondary evolutionary algorithms to process inferior individuals. Thus, EA4eigCS is obtained. In our ensemble, the secondary evolutionary algorithms are expected to vary distribution of the population for breaking stagnation. Experimental results show that our EA4eigCS outperforms EA4eig and is competitive when compared with state-of-the-art algorithms. Code and supplementary material are available at:https://anonymous.4open.science/r/EA4eigCS-2A43.

Related papers

• optimize_anything: A Universal API for Optimizing any Text Parameter [98.42497715725356]
  We show a single AI-based optimization system-supporting single-task search, multi-task search with cross-problem transfer, and generalization to unseen inputs.<n>We show for the first time that text optimization with LLM-based search is a general-purpose problem-solving paradigm.
  arXiv  Detail & Related papers  (2026-05-19T10:18:12Z)
• EvoX: Meta-Evolution for Automated Discovery [115.89434419482797]
  EvoX is an adaptive evolution method that optimize its own evolution process.<n>It continuously updates how prior solutions are selected and varied based on progress.<n>It outperforms existing AI-driven evolutionary methods including AlphaEvolve, OpenEvolve, GEPA, and ShinkaEvolve on the majority of tasks.
  arXiv  Detail & Related papers  (2026-02-26T18:54:41Z)
• Evolving Deep Learning Optimizers [0.0]
  We present a genetic algorithm framework for discovering deep learning optimization algorithms.<n>Our approach encodes genomes that specify combinations of primitive update terms.<n>We find an evolved that outperforms Adam by 2.6% in aggregate fitness.
  arXiv  Detail & Related papers  (2025-12-05T01:30:07Z)
• Ecological Neural Architecture Search [0.0]
  This paper introduces Neuvo Ecological Neural Architecture Search (ENAS)<n>ENAS incorporates evolutionary parameters directly into the candidate solutions' phenotypes, allowing them to evolve dynamically alongside architecture specifications.
  arXiv  Detail & Related papers  (2025-03-13T21:40:25Z)
• Modified-Improved Fitness Dependent Optimizer for Complex and Engineering Problems [5.078139820108554]
  Fitness dependent (FDO) is considered one of the novel swarm intelligent algorithms. This study proposes a modified version of IFDO, called M-IFDO. M-IFDO is compared against five state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2024-06-27T07:47:23Z)
• Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms [50.91348344666895]
  Evolutionary Reinforcement Learning (ERL) integrates Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) for optimization. This survey offers a comprehensive overview of the diverse research branches in ERL.
  arXiv  Detail & Related papers  (2024-01-22T14:06:37Z)
• 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)
• Massively Parallel Genetic Optimization through Asynchronous Propagation of Populations [50.591267188664666]
  Propulate is an evolutionary optimization algorithm and software package for global optimization. We provide an MPI-based implementation of our algorithm, which features variants of selection, mutation, crossover, and migration. We find that Propulate is up to three orders of magnitude faster without sacrificing solution accuracy.
  arXiv  Detail & Related papers  (2023-01-20T18:17:34Z)
• Improving RNA Secondary Structure Design using Deep Reinforcement Learning [69.63971634605797]
  We propose a new benchmark of applying reinforcement learning to RNA sequence design, in which the objective function is defined to be the free energy in the sequence's secondary structure. We show results of the ablation analysis that we do for these algorithms, as well as graphs indicating the algorithm's performance across batches.
  arXiv  Detail & Related papers  (2021-11-05T02:54:06Z)
• Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees [94.72461292387146]
  We propose to reformulate the result diversification problem as a bi-objective search problem, and solve it by a multi-objective evolutionary algorithm (EA) We theoretically prove that the GSEMO can achieve the optimal-time approximation ratio, $1/2$. When the objective function changes dynamically, the GSEMO can maintain this approximation ratio in running time, addressing the open question proposed by Borodin et al.
  arXiv  Detail & Related papers  (2021-10-18T14:00:22Z)
• Evolving Evolutionary Algorithms using Multi Expression Programming [0.0]
  Instead of evolving only the parameters of the algorithm we will evolve an entire EA capable of solving a particular problem. An nongenerational EA for function optimization is evolved in this paper. Numerical experiments show the effectiveness of this approach.
  arXiv  Detail & Related papers  (2021-08-22T09:30:57Z)
• On the Performance of Metaheuristics: A Different Perspective [0.0]
  We study some basic evolutionary and swam-intelligence metaheuristics i.e. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Teaching-Learning-Based Optimization (TLBO) and Cuckoo Optimization algorithm (COA) A large number of experiments have been conducted on 20 different optimization benchmark functions with different characteristics, and the results reveal to us some fundamental conclusions besides the following ranking order among these metaheuristics.
  arXiv  Detail & Related papers  (2020-01-24T09:34:10Z)

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.