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Updated: Apr 3, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Analysis of (1+1) evolutionary algorithm and randomized local search with memory.

Chi Wan Sung1, Shiu Yin Yuen

  • 1Department of Electronic Engineering, City University of Hong Kong, Hong Kong. albert.sung@cityu.edu.hk

Evolutionary Computation
|September 28, 2010
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Summary
This summary is machine-generated.

Introducing memory-assisted evolutionary algorithms and randomized local search, (1+1) EA-m and RLS-m+, significantly improve optimization performance. These algorithms offer practical enhancements for complex search problems.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Standard (1+1) evolutionary algorithms (EA) and randomized local search (RLS) are foundational optimization techniques.
  • The incorporation of memory into these algorithms aims to enhance their efficiency in exploring solution spaces.
  • Existing methods lack mechanisms to retain and utilize previously explored, albeit non-improving, solutions.

Purpose of the Study:

  • To introduce and analyze novel memory-assisted algorithms: (1+1) EA-m and RLS-m+.
  • To theoretically evaluate the performance improvements offered by these new algorithms on unimodal and multimodal functions.
  • To establish a unified mathematical framework for analyzing these algorithms, including the concept of spatially invariant neighborhoods.

Main Methods:

  • Development of (1+1) EA-m and RLS-m+ algorithms, incorporating memory to store previously visited solutions.
  • Rigorous theoretical analysis of the expected time to find globally optimal solutions for both unimodal and multimodal functions.
  • Proposal of a unified mathematical framework based on spatially invariant neighborhoods to generalize algorithm analysis.

Main Results:

  • For unimodal functions, memory assistance provides a positive but limited improvement (at most 50%).
  • For multimodal functions, memory assistance yields significant improvements, reducing the order of growth for complex functions and potentially changing exponential to polynomial complexity.
  • Empirical results confirm the superiority of (1+1) EA-m and RLS-m+ over their non-memory counterparts, especially RLS-m+ which enhances RLS practicality without extra memory.

Conclusions:

  • The developed memory-assisted algorithms, (1+1) EA-m and RLS-m+, offer substantial performance gains in optimization.
  • These algorithms represent simple yet effective forms of tabu search and show promise for integration into memetic algorithms.
  • RLS-m+ notably makes randomized local search more practical for a wider range of problems.