Evolutionary Algorithms and Engineering Applications: A Comprehensive Survey of Classical Methods and Emerging Trends
進化的アルゴリズムと工学的応用：古典的手法と新興トレンドの包括的サーベイ (AI 翻訳)
Hasan M, Yasmin F
🤖 gxceed AI 要約
日本語
本サーベイは、進化的アルゴリズム（EA）の工学分野への応用を体系的にレビューする。再生可能エネルギー、構造工学、産業最適化など多岐にわたる領域を取り上げ、古典的手法から深層学習統合までを網羅。スケーラビリティや制約処理といった課題も議論。
English
This survey provides a structured review of evolutionary algorithms (EAs) for engineering problems, covering applications in renewable energy, civil engineering, industrial optimization, and more. It discusses classical methods and emerging trends like EA-deep learning integration, along with challenges such as scalability and constraint handling.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では、再生可能エネルギー分野の設備最適化や電力系統制御にEAが応用可能。本サーベイは手法選択の基礎資料として有用。
In the global GX context
Globally, EAs are key tools for optimizing renewable energy systems and industrial processes. This survey provides a comprehensive reference for researchers and engineers applying EA in energy transition contexts.
👥 読者別の含意
🔬研究者:A broad overview of EA variants and their engineering applications, useful for identifying suitable methods for optimization problems in GX-related fields.
🏢実務担当者:Provides a taxonomy and application cases that can guide selection of EA techniques for real-world engineering optimization, including renewable energy.
📄 Abstract（原文）
Evolutionary algorithms (EAs) are widely used nature-inspired optimization methods capable of solving complex and high-dimensional problems across science and engineering. Foundational paradigms such as genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming have expanded into multi-objective, surrogate-assisted, hybrid, and large-scale variants, broadening their applicability to dynamic and datadriven environments. This survey provides a structured review of EAs from a domain-centric perspective, focusing on how different techniques are designed for engineering problems. Applications are examined across renewable energy, civil and structural engineering, electronics, industrial optimization, healthcare, robotics, and smart cities. We present an updated taxonomy of classical and emerging algorithms, consolidate recent application studies, and review benchmarking and reproducibility practices essential for fair evaluation. Key challenges including scalability, constraint handling, and exploration–exploitation balance are discussed alongside future directions such as EA–deep learning integration, federated optimization, and interpretable evolution. This survey offers an updated view of EAs and their engineering relevance.
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.20944/preprints202606.0126.v1first seen 2026-06-04 04:24:39 · last seen 2026-06-16 04:30:26
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。