First Steps Towards a Runtime Analysis When Starting With a Good Solution
Fuente:
arXiv
Saved in:
| Main Authors: | Antipov, Denis, Buzdalov, Maxim, Doerr, Benjamin |
|---|---|
| Format: | Preprint |
| Published: |
2020
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Tight Runtime Guarantees From Understanding the Population Dynamics of the GSEMO Multi-Objective Evolutionary Algorithm
by: Doerr, Benjamin, et al.
Published: (2025)
by: Doerr, Benjamin, et al.
Published: (2025)
Lazy Parameter Tuning and Control: Choosing All Parameters Randomly From a Power-Law Distribution
by: Antipov, Denis, et al.
Published: (2021)
by: Antipov, Denis, et al.
Published: (2021)
OneMax is not the Easiest Function for Fitness Improvements
by: Kaufmann, Marc, et al.
Published: (2022)
by: Kaufmann, Marc, et al.
Published: (2022)
How Population Diversity Influences the Efficiency of Crossover
by: Cerf, Sacha, et al.
Published: (2024)
by: Cerf, Sacha, et al.
Published: (2024)
Towards a Rigorous Understanding of the Population Dynamics of the NSGA-III: Tight Runtime Bounds
by: Opris, Andre
Published: (2025)
by: Opris, Andre
Published: (2025)
Hardest Monotone Functions for Evolutionary Algorithms
by: Kaufmann, Marc, et al.
Published: (2023)
by: Kaufmann, Marc, et al.
Published: (2023)
A First Runtime Analysis of the PAES-25: An Enhanced Variant of the Pareto Archived Evolution Strategy
by: Opris, Andre
Published: (2025)
by: Opris, Andre
Published: (2025)
Survey of Genetic and Differential Evolutionary Algorithm Approaches to Search Documents Based On Semantic Similarity
by: Muniyappa, Chandrashekar, et al.
Published: (2025)
by: Muniyappa, Chandrashekar, et al.
Published: (2025)
Runtime Analyses of NSGA-III on Many-Objective Problems: Provable Exponential Speedup via Stochastic Population Update
by: Opris, Andre
Published: (2025)
by: Opris, Andre
Published: (2025)
Parameter Tuning of the Firefly Algorithm by Standard Monte Carlo and Quasi-Monte Carlo Methods
by: Joy, Geethu, et al.
Published: (2024)
by: Joy, Geethu, et al.
Published: (2024)
Domain-Informed Genetic Superposition Programming: A Case Study on SFRC Beams
by: Khorshidi, Mohammad Sadegh, et al.
Published: (2025)
by: Khorshidi, Mohammad Sadegh, et al.
Published: (2025)
Runtime Analyses of NSGA-III on Many-Objective Problems
by: Opris, Andre, et al.
Published: (2024)
by: Opris, Andre, et al.
Published: (2024)
Evolutionary Algorithms Are Significantly More Robust to Noise When They Ignore It
by: Antipov, Denis, et al.
Published: (2024)
by: Antipov, Denis, et al.
Published: (2024)
Achieving Tight $O(4^k)$ Runtime Bounds on Jump$_k$ by Proving that Genetic Algorithms Evolve Near-Maximal Population Diversity
by: Opris, Andre, et al.
Published: (2024)
by: Opris, Andre, et al.
Published: (2024)
Quantum-Inspired Evolutionary Algorithms for Feature Subset Selection: A Comprehensive Survey
by: Vivek, Yelleti, et al.
Published: (2024)
by: Vivek, Yelleti, et al.
Published: (2024)
Improved Differential Evolution based Feature Selection through Quantum, Chaos, and Lasso
by: Vivek, Yelleti, et al.
Published: (2024)
by: Vivek, Yelleti, et al.
Published: (2024)
Evolutionary Computation as Natural Generative AI
by: Shi, Yaxin, et al.
Published: (2025)
by: Shi, Yaxin, et al.
Published: (2025)
L-System Genetic Encoding for Scalable Neural Network Evolution: A Comparison with Direct Matrix Encoding
by: Stuy, Alexander, et al.
Published: (2026)
by: Stuy, Alexander, et al.
Published: (2026)
Optimizing Genetic Algorithms Using the Binomial Distribution
by: Cicirello, Vincent A.
Published: (2024)
by: Cicirello, Vincent A.
Published: (2024)
When Switching Algorithms Helps: A Theoretical Study of Online Algorithm Selection
by: Antipov, Denis, et al.
Published: (2026)
by: Antipov, Denis, et al.
Published: (2026)
A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization
by: Cenikj, Gjorgjina, et al.
Published: (2024)
by: Cenikj, Gjorgjina, et al.
Published: (2024)
Massively Parallel Genetic Optimization through Asynchronous Propagation of Populations
by: Taubert, Oskar, et al.
Published: (2023)
by: Taubert, Oskar, et al.
Published: (2023)
Open Source Evolutionary Computation with Chips-n-Salsa
by: Cicirello, Vincent A.
Published: (2024)
by: Cicirello, Vincent A.
Published: (2024)
Quantum-Inspired Genetic Optimization for Patient Scheduling in Radiation Oncology
by: SaiToh, Akira, et al.
Published: (2025)
by: SaiToh, Akira, et al.
Published: (2025)
SCAPE: Searching Conceptual Architecture Prompts using Evolution
by: Lim, Soo Ling, et al.
Published: (2024)
by: Lim, Soo Ling, et al.
Published: (2024)
Enhancing Parameter Control Policies with State Information
by: Covini, Gianluca, et al.
Published: (2025)
by: Covini, Gianluca, et al.
Published: (2025)
Already Moderate Population Sizes Provably Yield Strong Robustness to Noise
by: Antipov, Denis, et al.
Published: (2024)
by: Antipov, Denis, et al.
Published: (2024)
Runtime Analysis of the SMS-EMOA for Many-Objective Optimization
by: Zheng, Weijie, et al.
Published: (2023)
by: Zheng, Weijie, et al.
Published: (2023)
Isomorphic Functionalities between Ant Colony and Ensemble Learning: Part III -- Gradient Descent, Neural Plasticity, and the Emergence of Deep Intelligence
by: Fokoué, Ernest, et al.
Published: (2026)
by: Fokoué, Ernest, et al.
Published: (2026)
Proven Runtime Guarantees for How the MOEA/D Computes the Pareto Front From the Subproblem Solutions
by: Doerr, Benjamin, et al.
Published: (2024)
by: Doerr, Benjamin, et al.
Published: (2024)
Runtime Analysis of the Compact Genetic Algorithm on the LeadingOnes Benchmark
by: Chwiałkowski, Marcel, et al.
Published: (2025)
by: Chwiałkowski, Marcel, et al.
Published: (2025)
Runtime Analysis for Multi-Objective Evolutionary Algorithms in Unbounded Integer Spaces
by: Doerr, Benjamin, et al.
Published: (2024)
by: Doerr, Benjamin, et al.
Published: (2024)
Near-Tight Runtime Guarantees for Many-Objective Evolutionary Algorithms
by: Wietheger, Simon, et al.
Published: (2024)
by: Wietheger, Simon, et al.
Published: (2024)
CLEAR: Cue Learning using Evolution for Accurate Recognition Applied to Sustainability Data Extraction
by: Bentley, Peter J., et al.
Published: (2025)
by: Bentley, Peter J., et al.
Published: (2025)
First Mathematical Runtime Analyses of Multi-Objective Evolutionary Algorithms for Multi-Valued Decision Variables
by: Li, Mingfeng, et al.
Published: (2026)
by: Li, Mingfeng, et al.
Published: (2026)
Improved Runtime Guarantees for the SPEA2 Multi-Objective Optimizer
by: Doerr, Benjamin, et al.
Published: (2025)
by: Doerr, Benjamin, et al.
Published: (2025)
Runtime Analysis for the NSGA-II: Proving, Quantifying, and Explaining the Inefficiency For Many Objectives
by: Zheng, Weijie, et al.
Published: (2022)
by: Zheng, Weijie, et al.
Published: (2022)
Many Objective Problems Where Crossover is Provably Essential
by: Opris, Andre
Published: (2024)
by: Opris, Andre
Published: (2024)
A First Step Towards Runtime Analysis of Evolutionary Neural Architecture Search
by: Lv, Zeqiong, et al.
Published: (2024)
by: Lv, Zeqiong, et al.
Published: (2024)
Runtime Analysis for Permutation-based Evolutionary Algorithms
by: Doerr, Benjamin, et al.
Published: (2022)
by: Doerr, Benjamin, et al.
Published: (2022)
Similar Items
-
Tight Runtime Guarantees From Understanding the Population Dynamics of the GSEMO Multi-Objective Evolutionary Algorithm
by: Doerr, Benjamin, et al.
Published: (2025) -
Lazy Parameter Tuning and Control: Choosing All Parameters Randomly From a Power-Law Distribution
by: Antipov, Denis, et al.
Published: (2021) -
OneMax is not the Easiest Function for Fitness Improvements
by: Kaufmann, Marc, et al.
Published: (2022) -
How Population Diversity Influences the Efficiency of Crossover
by: Cerf, Sacha, et al.
Published: (2024) -
Towards a Rigorous Understanding of the Population Dynamics of the NSGA-III: Tight Runtime Bounds
by: Opris, Andre
Published: (2025)