References and Further Reading#

This section gathers essential resources for those who wish to dive deeper into the concepts behind Genetic Algorithms (GAs), as well as related areas such as Data Science and Artificial Intelligence. It includes academic references, open-access libraries, scientific journals, and the monograph that led to the creation of the GADemo platform.


Foundational Texts on Genetic Algorithms#

  • “Genetic Algorithms in Search, Optimization, and Machine Learning” by David E. Goldberg
    This comprehensive book introduces the theory and application of genetic algorithms, serving as a cornerstone in the field.
    View on Google Books

  • “Algoritmos Genéticos” by Ricardo Linden
    Publisher’s page

🎓 Some Academic Publications and Thesis#


Open-access Repositories and Digital Libraries#

  • CiteSeerX
    Scientific search engine and digital library focused on literature in computer science and information systems.

  • Redalyc
    Network of scientific journals from Latin America, Spain, and Portugal offering free access to high-quality academic papers.

  • DBLP Comprehensive bibliography covering computer science research articles and conference papers.


Scientific Journals#

  • Applied Sciences – MDPI (AI & Robotics Section)
    Peer-reviewed open access journal publishing practical applications of artificial intelligence, robotics, and evolutionary computation.

  • Algorithms – MDPI
    Focuses on the design, analysis, and application of algorithms, including genetic algorithms and other metaheuristics.

  • Frontiers in Artificial Intelligence
    A multidisciplinary journal exploring deep learning, neuroevolution, and intelligent systems, with all content freely accessible.

  • PeerJ Computer Science
    Open access journal covering computer vision, machine learning, and evolutionary algorithms with code-sharing incentives.

  • PLOS Computational Biology
    High-impact open access journal connecting biology with computational intelligence, including applications of genetic algorithms and neural networks.


🧠 These references aim to support both beginners and advanced learners in deepening their understanding of Genetic Algorithms and beyond.