Full Inclusive Genetic Programming

Francesco Marchetti, Mauro Castelli, Illya Bakurov, Leonardo Vanneschi

Research output: Contribution to conferencePaperpeer-review


This manuscript presents an improved version of the Inclusive Genetic Programming (IGP) algorithm. The IGP was developed to promote and maintain the population’s genotypic diversity and showed superior performance compared to standard Genetic Programming (GP). In this work, two modifications to the IGP are proposed: first, the diversity promotion and maintenance mechanism is enhanced with information from the phenotype of the individuals rather than only the genotype; second, the Evolutionary Demes Despeciation Algorithm - V2 (EDDA-V2) is used to initialize the population. The phenotype is considered to differentiate the individuals also according to their behaviour rather than only their structure, while EDDA-V2 is employed to start the evolutionary process with a more diverse population than the one generated with traditional initialization mechanisms. The algorithms incorporating these improvements are called Full Inclusive Genetic Programming (FIGP) and FIGP E, respectively with and without the EDDA-V2 initialization. The experimental results, performed over eight benchmarks and considering six algorithms, demonstrate the superior performance of FIGP and FIGP E in comparison to other GP formulations. Moreover, the EDDA-V2 initialization allows for a significant reduction of the computational time.
Original languageEnglish
Publication statusAccepted/In press - 1 Jan 2024
EventIEEE Congress on Evolutionary Computation 2024 - Pacifico Yokohama Conference Center & North, Yokohama, Japan
Duration: 30 Jun 20245 Jul 2024


ConferenceIEEE Congress on Evolutionary Computation 2024
Abbreviated titleCEC 2024
Internet address


  • Genetic Programming
  • Population’s Diversity
  • Symbolic Regression
  • PMLB Benchmarks
  • Population Initialization


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