Open Source Evolutionary Computation with Chips-n-Salsa
- URL: http://arxiv.org/abs/2412.02004v1
- Date: Mon, 02 Dec 2024 22:18:31 GMT
- Title: Open Source Evolutionary Computation with Chips-n-Salsa
- Abstract summary: This paper concerns the evolutionary algorithms that Chips-n-Salsa now provides.<n>Chips-n-Salsa's evolutionary algorithms include implementations with adaptive mutation and crossover rates.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: When it was first introduced, the Chips-n-Salsa Java library provided stochastic local search and related algorithms, with a focus on self-adaptation and parallel execution. For the past four years, we expanded its scope to include evolutionary computation. This paper concerns the evolutionary algorithms that Chips-n-Salsa now provides, which includes multiple evolutionary models, common problem representations, a wide range of mutation and crossover operators, and a variety of benchmark problems. Well-defined Java interfaces enable easily integrating custom representations and evolutionary operators, as well as defining optimization problems. Chips-n-Salsa's evolutionary algorithms include implementations with adaptive mutation and crossover rates, as well as both sequential and parallel execution. Source code is maintained on GitHub, and immutable artifacts are regularly published to the Maven Central Repository to enable easily importing into projects for reproducible builds. Effective development processes such as test-driven development, as well as a variety of static analysis tools help ensure code quality.
Related papers
- AdaExplore: Failure-Driven Adaptation and Diversity-Preserving Search for Efficient Kernel Generation [59.964989458924585]
AdaExplore is an agent framework that enables self-improvement via accumulated execution feedback for performance-critical kernel code generation.<n>In the adaptation stage, the agent synthesizes tasks and converts recurring failures into a reusable memory of validity rules.<n>In the search stage, the agent organizes candidate kernels as a tree and alternates between small local refinements and larger structural regeneration.
arXiv Detail & Related papers (2026-04-17T18:25:03Z) - BEAM: Bi-level Memory-adaptive Algorithmic Evolution for LLM-Powered Heuristic Design [49.50107918295052]
Large Language Model-based Hyper Heuristic (LHH) has recently emerged as an efficient way for automatic design.<n>We propose textbfBEAM (Bi-level Memory-adaptive Algorithmic Evolution) to address this problem.<n>We show that BEAM significantly outperforms existing LHHs, notably reducing the optimality gap by 37.84% on aggregate.
arXiv Detail & Related papers (2026-04-14T15:46:47Z) - AdaEvolve: Adaptive LLM Driven Zeroth-Order Optimization [61.535567824938205]
We introduce AdaEvolve, a framework that reformulates LLM-driven evolution as a hierarchical adaptive optimization problem.<n>AdaEvolve consistently outperforms the open-ended baselines across 185 different open-ended optimization problems.
arXiv Detail & Related papers (2026-02-23T18:45:31Z) - Large Language Model-Powered Evolutionary Code Optimization on a Phylogenetic Tree [17.08113692977552]
PhyloEvolve is a system that reframes GPU-oriented algorithm optimization as an In-Context Reinforcement Learning problem.<n>We introduce a phylogenetic tree representation that captures inheritance, divergence, and recombination among algorithm variants.<n>We evaluate PhyloEvolve on scientific computing workloads including PDE solvers, manifold learning, and spectral graph algorithms.
arXiv Detail & Related papers (2026-01-20T22:32:52Z) - Controlled Self-Evolution for Algorithmic Code Optimization [33.82967000330864]
Self-evolution methods enhance code generation through iterative "generate-verify-refine" cycles.<n>Existing approaches fail to discover solutions with superior complexity within limited budgets.<n>We propose Controlled Self-Evolution (CSE), which consists of three key components.
arXiv Detail & Related papers (2026-01-12T09:23:13Z) - irace-evo: Automatic Algorithm Configuration Extended With LLM-Based Code Evolution [1.1313571663082607]
irace-evo is an extension of irace that integrates code evolution through large language models (LLMs)<n>We evaluate irace-evo on the Construct, Merge, Solve & Adapt (CMSA) metaheuristic for the Variable-Sized Bin Packing Problem (VSBPP)
arXiv Detail & Related papers (2025-11-15T12:42:18Z) - SWE-Bench-CL: Continual Learning for Coding Agents [0.0]
SWE-Bench-CL is a novel continual learning benchmark built on the human-verified SWE-Bench Verified dataset.<n>By organizing GitHub issues into chronologically ordered sequences that reflect natural repository evolution, SWE-Bench-CL enables direct evaluation of an agent's ability to accumulate experience.
arXiv Detail & Related papers (2025-06-13T07:11:14Z) - Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale [68.6602625868888]
We introduce convolutional multi-hybrid architectures, with a design grounded on two simple observations.
Operators in hybrid models can be tailored to token manipulation tasks such as in-context recall, multi-token recall, and compression.
We train end-to-end 1.2 to 2.9 times faster than optimized Transformers, and 1.1 to 1.4 times faster than previous generation hybrids.
arXiv Detail & Related papers (2025-02-25T19:47:20Z) - Fast Genetic Algorithm for feature selection -- A qualitative approximation approach [5.279268784803583]
We propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection.
We show that CHCQX converges faster to feature subset solutions of significantly higher accuracy, particularly for large datasets with over 100K instances.
arXiv Detail & Related papers (2024-04-05T10:15:24Z) - GloptiNets: Scalable Non-Convex Optimization with Certificates [61.50835040805378]
We present a novel approach to non-cube optimization with certificates, which handles smooth functions on the hypercube or on the torus.
By exploiting the regularity of the target function intrinsic in the decay of its spectrum, we allow at the same time to obtain precise certificates and leverage the advanced and powerful neural networks.
arXiv Detail & Related papers (2023-06-26T09:42:59Z) - Self Optimisation and Automatic Code Generation by Evolutionary
Algorithms in PLC based Controlling Processes [0.0]
A novel approach based on evolutionary algorithms is proposed to self optimise the system logic of complex processes.
The presented approach is evaluated on an industrial liquid station process subject to a multi-objective problem.
arXiv Detail & Related papers (2023-04-12T06:36:54Z) - Performance Embeddings: A Similarity-based Approach to Automatic
Performance Optimization [71.69092462147292]
Performance embeddings enable knowledge transfer of performance tuning between applications.
We demonstrate this transfer tuning approach on case studies in deep neural networks, dense and sparse linear algebra compositions, and numerical weather prediction stencils.
arXiv Detail & Related papers (2023-03-14T15:51:35Z) - EvoTorch: Scalable Evolutionary Computation in Python [1.8514314381314885]
EvoTorch is an evolutionary computation library designed to work with high-dimensional optimization problems.
EvoTorch is based on and seamlessly works with the PyTorch library, and therefore, allows the users to define their optimization problems using a well-known API.
arXiv Detail & Related papers (2023-02-24T12:37:45Z) - EvoX: A Distributed GPU-accelerated Framework for Scalable Evolutionary
Computation [40.71953374838183]
EvoX is a computing framework tailored for automated, distributed, and heterogeneous execution of EC algorithms.
At the core of EvoX lies a unique programming model to streamline the development of parallelizable EC algorithms.
EvoX offers comprehensive support for a diverse set of benchmark problems, ranging from dozens of numerical test functions to hundreds of reinforcement learning tasks.
arXiv Detail & Related papers (2023-01-29T15:00:16Z) - 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) - evosax: JAX-based Evolution Strategies [0.0]
We release evosax: a JAX-based library of evolutionary optimization algorithms.
evosax implements 30 evolutionary optimization algorithms including finite-difference-based, estimation-of-distribution evolution strategies and various genetic algorithms.
It is designed in a modular fashion and allows for flexible usage via a simple ask-evaluate-tell API.
arXiv Detail & Related papers (2022-12-08T10:34:42Z) - X-volution: On the unification of convolution and self-attention [52.80459687846842]
We propose a multi-branch elementary module composed of both convolution and self-attention operation.
The proposed X-volution achieves highly competitive visual understanding improvements.
arXiv Detail & Related papers (2021-06-04T04:32:02Z) - AdaLead: A simple and robust adaptive greedy search algorithm for
sequence design [55.41644538483948]
We develop an easy-to-directed, scalable, and robust evolutionary greedy algorithm (AdaLead)
AdaLead is a remarkably strong benchmark that out-competes more complex state of the art approaches in a variety of biologically motivated sequence design challenges.
arXiv Detail & Related papers (2020-10-05T16:40:38Z)
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.