On the Topology of Genetic Algorithms
Authors: David Hofmeyr
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper introduces a topological structure for the search space which is consistent with existing theory and practice for genetic algorithms, namely forma analysis. A notion of convexity is defined within this context and connections between this definition and forma analysis are established. This framework provides an alternative perspective on the exploitation/exploration dilemma as well as population convergence, which relates directly to the genetic operators employed to drive the evolution process. It also provides a different interpretation of design constraints associated with genetic algorithm implementations. The intention is to provide a new analytical perspective for genetic algorithms, and to establish a connection with exact search methods through the concept of convexity. |
| Researcher Affiliation | Academia | David Hofmeyr Lancaster University, Lancaster, UK d.hofmeyr@lancaster.ac.uk |
| Pseudocode | No | No structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures) were found. |
| Open Source Code | No | No concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper was provided. The paper is theoretical and does not discuss implementation. |
| Open Datasets | No | No concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset was provided. The paper is theoretical and does not use datasets. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning was provided. The paper is theoretical and does not use datasets. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running experiments were provided. The paper is theoretical and does not involve computational experiments. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment were provided. The paper is theoretical and does not involve software implementation. |
| Experiment Setup | No | No specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) were found in the main text. The paper is theoretical and does not describe experimental setups. |