This paper proposes a Genetic Algorithm with constraint repair mechanism for optimally allocating educational resources to high school mathematics students under study time constraints.

This paper analyzes the runtime of a genetic algorithm on Jump$_k$ benchmark functions using limit theorems from probability theory, providing a tighter upper bound on escape time than previous work.

This paper compares the effect of different initialization methods on the accuracy and complexity of solutions in genetic programming for symbolic regression, finding no significant differences.

This paper enhances a genetic algorithm approach for solving the Shortest Vector Problem (SVP) in both integral and module lattices by incorporating domain-informed representation and crossover.

This paper enhances a genetic algorithm approach for solving the Shortest Vector Problem (SVP) in lattices by incorporating domain-informed representation, thereby extending its applicability to modul…

The paper introduces a hybrid WGAN-GA framework that uses a Genetic Algorithm (GA) to refine graphs generated by a GAN, significantly reducing structural deviations and improving realism.

This paper provides theoretical analysis of parameter settings for the bat algorithm using dynamical systems and population variance theory, and validates the results through numerical experiments.

This paper investigates the benefits of generating multiple solutions in each generation for Evolutionary Diversity Optimisation (EDO) and proposes efficient methods to achieve it.

This paper analyzes the performance and explainability requirements of evolutionary algorithms when applied to complex, real-world physics-informed optimization problems, identifying a gap between cur…

The Beagle framework is a GPU-based genetic programming tool for symbolic regression problems.

The paper empirically and theoretically demonstrates that incorporating Lamarckian and Baldwinian mechanisms into evolutionary algorithms significantly outperforms standard Darwinian evolution, especi…

The paper demonstrates the potential of using LLMs within verifier-guided evolutionary coding agents to develop and improve algorithms, specifically applied to contraction order optimization in tensor…

This paper introduces the first LLM-generated, domain-independent heuristics for symbolic AI planning, using evolutionary search to surpass the performance of hand-engineered state-of-the-art methods.

This paper demonstrates that optimizing hyperparameters for two specific recombination operators can significantly improve the performance of Cartesian Genetic Programming, which traditionally relies…

The paper investigates the ability of evolutionary computation to discover monotone Boolean functions with high nonlinearity, demonstrating that genetic programming is a highly effective encoding for…

The paper establishes conditions for QUBO formulations of combinatorial optimization problems that guarantee valid binary and feasible local minimizers using gradient-based methods.

This book provides a compact, derivation-oriented mathematical primer that connects major families of generative AI models, showing their underlying structural relationships.

This paper benchmarks zero-shot synthesis of parent-selection operators across eight large language models and finds that Claude Sonnet~4.6 and Gemini~3.1 Pro perform strongly, with the best operator…