Evolve On Click (EvOC) - An Intuitive Web Platform to Collaboratively Implement, Execute, and Visualize Evolutionary Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@InProceedings{murali:2025:GECCOcomp,

• author = "Ritwik Murali and Ashwin Narayanan Sivamani and Abhinav Ramakrishnan and Hariharan Arul and Ananya R",
• title = "Evolve On Click {(EvOC)} - An Intuitive Web Platform to Collaboratively Implement, Execute, and Visualize Evolutionary Algorithms",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion",
• year = "2025",
• editor = "Carola Doerr and Mike Preuss",
• pages = "147--150",
• address = "Malaga, Spain",
• series = "GECCO '25 Companion",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, evolutionary algorithms, distributed artificial intelligence, distributed evolutionary algorithms in python, DEAP, software architectures, evolutionary computation, Benchmarking, Benchmarks, Software, Reproducibility: Poster",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3726652",
• DOI = "10.1145/3712255.3726652",
• size = "4 pages",
• abstract = "This paper proposes {"}Evolve On Click{"} (EvOC) - an open-source intuitive web-based platform to simplify the implementation, execution, and visualization of Evolutionary Algorithms (EAs) including genetic programming, by providing a user-friendly interface. This facilitates easier accessibility of evolutionary algorithm software packages such as DEAP, to users with minimal programming experience. EvOC guides users through the EA design process, allowing them to experiment with different algorithms, parameters, and configurations without the need for programming expertise. The platform also incorporates features to show code created based on the configuration so that users can also learn from it, thus enhancing collaboration and enabling users to easily share their results with others. The architecture used by EvOC also supports ease of access for parallel and distributed EAs with real-time log streaming / monitoring and visualization of the evolution runs. By incorporating the latest DevOps techniques during the development process, EvOC does not require extensive maintenance and allows for the platform to be run as a service, supporting multiple users on a single instance. This paper details the design, implementation, and evaluation of EvOC towards increasing accessibility and ease of comfort with EAs for novice learners - thus broadening the reach of the community.",
• notes = "GECCO-2025 BBSR A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Ritwik Murali Ashwin Narayanan Sivamani Abhinav Ramakrishnan Hariharan Arul Ananya R

Citations