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The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathways

Daniel Molina, Javier Del Ser, Javier Poyatos, Francisco Herrera

TL;DR

The paper investigates the paradox of success in bioinspired and evolutionary optimization, where many new solvers lack genuine novelty and benchmarking practices are imperfect. It synthesizes historical critiques and current methodological pathways to enhance algorithmic innovation, fair comparability, and replicability. It surveys automated design approaches, including decomposition, equivalence detection, and large language model–assisted methods, as routes to more robust solvers. By foregrounding real-world problems and standardized evaluation, it argues for a shift toward innovations with tangible practical impact in diverse domains.

Abstract

Evolutionary and bioinspired computation are crucial for efficiently addressing complex optimization problems across diverse application domains. By mimicking processes observed in nature, like evolution itself, these algorithms offer innovative solutions beyond the reach of traditional optimization methods. They excel at finding near-optimal solutions in large, complex search spaces, making them invaluable in numerous fields. However, both areas are plagued by challenges at their core, including inadequate benchmarking, problem-specific overfitting, insufficient theoretical grounding, and superfluous proposals justified only by their biological metaphor. This overview recapitulates and analyzes in depth the criticisms concerning the lack of innovation and rigor in experimental studies within the field. To this end, we examine the judgmental positions of the existing literature in an informed attempt to guide the research community toward directions of solid contribution and advancement in these areas. We summarize guidelines for the design of evolutionary and bioinspired optimizers, the development of experimental comparisons, and the derivation of novel proposals that take a step further in the field. We provide a brief note on automating the process of creating these algorithms, which may help align metaheuristic optimization research with its primary objective (solving real-world problems), provided that our identified pathways are followed. Our conclusions underscore the need for a sustained push towards innovation and the enforcement of methodological rigor in prospective studies to fully realize the potential of these advanced computational techniques.

The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathways

TL;DR

The paper investigates the paradox of success in bioinspired and evolutionary optimization, where many new solvers lack genuine novelty and benchmarking practices are imperfect. It synthesizes historical critiques and current methodological pathways to enhance algorithmic innovation, fair comparability, and replicability. It surveys automated design approaches, including decomposition, equivalence detection, and large language model–assisted methods, as routes to more robust solvers. By foregrounding real-world problems and standardized evaluation, it argues for a shift toward innovations with tangible practical impact in diverse domains.

Abstract

Evolutionary and bioinspired computation are crucial for efficiently addressing complex optimization problems across diverse application domains. By mimicking processes observed in nature, like evolution itself, these algorithms offer innovative solutions beyond the reach of traditional optimization methods. They excel at finding near-optimal solutions in large, complex search spaces, making them invaluable in numerous fields. However, both areas are plagued by challenges at their core, including inadequate benchmarking, problem-specific overfitting, insufficient theoretical grounding, and superfluous proposals justified only by their biological metaphor. This overview recapitulates and analyzes in depth the criticisms concerning the lack of innovation and rigor in experimental studies within the field. To this end, we examine the judgmental positions of the existing literature in an informed attempt to guide the research community toward directions of solid contribution and advancement in these areas. We summarize guidelines for the design of evolutionary and bioinspired optimizers, the development of experimental comparisons, and the derivation of novel proposals that take a step further in the field. We provide a brief note on automating the process of creating these algorithms, which may help align metaheuristic optimization research with its primary objective (solving real-world problems), provided that our identified pathways are followed. Our conclusions underscore the need for a sustained push towards innovation and the enforcement of methodological rigor in prospective studies to fully realize the potential of these advanced computational techniques.
Paper Structure (18 sections, 1 figure)

This paper contains 18 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Known Critical Issues: From Lack of Innovation to Low-Quality Experimental Studies
  3. On the Lack of Algorithmic Innovation
  4. On the Low Quality of Experimental Studies
  5. On the Use of Poor Benchmarks
  6. Separation of Wheat from Chaff: Distinction of Weak Proposals
  7. On the Distinction of Weak Proposals
  8. Pathways to Detect Weak Proposals
  9. Fair and Right Comparisons in Bioinspired and Evolutionary Optimization: Replicability and Benchmarks
  10. Considerations in Real-World Problems
  11. Pathways to ensure Fair and Right Experimental Comparisons
  12. Pathways to support the Replicability of Experimental Studies
  13. Pathways to guarantee Proper Benchmarks
  14. Methodologies and Pathways for Improving Existing Bioinspired Optimization Algorithms
  15. A Smart and Promising Solution for overcoming Known Critical Issues: Automated Design of Metaheuristic Algorithms
  16. ...and 3 more sections

Figures (1)

  • Figure 1: Graphical summary of the issues, pathways and promising directions discussed in this manuscript.