Dr. Alexander Lalejini
Research Interests
Dr. Lalejini's research intersects computer science and evolutionary biology, applying the principles of each field to advance the other. Broadly, his work spans two areas of focus: (1) developing digital systems to investigate fundamental questions about evolutionary processes and how complex traits and behaviors evolve, and (2) applying our understanding of evolution to engineer search algorithms for solving problems.
Ideally, we would study the evolution of complex life by conducting experiments on organisms in their natural habitats, but the timescales and the data needed are typically unrealistic to obtain. As such, evolution experiments are often conducted with microbial populations in laboratory settings where general hypotheses about evolutionary processes can be tested in real time. However, laboratory studies with microbes still have several limitations, including the timescales required to observe substantial evolutionary change (months, years, or even decades), the low resolution that we can collect data, and the idiosyncrasies of living model systems (e.g., our inability to grow many microbial species in a laboratory setting). Digital evolution instantiates laboratory experiments in silico. Dr. Lalejini builds agent-based digital evolution models to empirically test hypotheses that would otherwise be relegated to theoretical analyses.
Evolutionary computing applies the principles of natural evolution as a general purpose search algorithm for solving challenging computational and engineering problems. Evolutionary search algorithms begin with an initial population of candidate solutions. Each generation, individuals are evaluated on one or more "fitness" criteria, and then promising individuals are selected (using a "parent selection algorithm") to be used to construct the next generation of candidate solutions. Dr. Lalejini uses his expertise in evolutionary biology to characterize the search properties of existing evolutionary algorithms and to develop novel evolutionary search methods. Dr. Lalejini also works to apply insights from his work in digital evolution and evolutionary computing to improve laboratory protocols for directing the evolution of microbial populations.
Professional Activities
Member of the International Society for Artificial Life.
Reviewer for journals and conferences, including Artificial Life, Scientific Reports, PeerJ CS, Frontiers in Ecology and Evolution, the Genetic and Evolutionary Computing Conference, and the Artificial Life Conference.
Organizer for the Genetic Programming in Theory and Practice workshop. Organized conference tutorials on topics such as tracking phylogenies in digital evolution systems, data standards, and creating good supplemental materials for peer-reviewed articles.
Personal Interests
Outside of GVSU, Dr. Lalejini enjoys exploring local eats, traveling, hiking, and playing board games. Dr. Lalejini is always interested in hearing about other folks' favorite meals and places to visit.
Teaching Interests
Algorithm Engineering
Evolutionary computing
Introduction to programming
Computer Organization
Multi-agent systems.
Recent Publications
See Google Scholar for a more complete list of publications: https://scholar.google.com/citations?user=sb89k5kAAAAJ&hl=en
- John Shea, Sydney Leither, Max Foreback, Emily Dolson, and Alexander Lalejini (2024). Environmental connectivity influences the origination of adaptive processes. In Proceedings of the 2024 Conference on Artificial Life (ALife). https://doi.org/10.1162/isal_a_00749
- Alexander Lalejini, Marcos Sanson, Jack Garbus, Matthew Andres Moreno, and Emily Dolson (2024). Runtime phylogenetic analysis enables extreme subsampling for test-based problems. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation (GECCO). https://doi.org/10.1145/3638530.3654208
- Anya Vostinar, Alexander Lalejini, Charles Ofria, Emily Dolson, and Matthew Andres Moreno (2024). Empirical: A scientific software library for research, education, and public engagement. Journal of Open Source Software, 9(98), 6617. https://doi.org/10.21105/joss.06617.
- Alexander Lalejini, Emily Dolson, Anya E Vostinar, and Luis Zaman (2022). Artificial selection methods from evolutionary computing show promise for directed evolution of microbes eLife 11:e79665. https://doi.org/10.7554/eLife.79665
Research with Students
- Grant Gordon (undergraduate): "Investigating the role of environmental connectivity on long-term evolutionary out-comes"
- Marcos Sanson (undergraduate): "Exploiting runtime phylogenetic analysis for evolutionary algorithms"
- John Shea (undergraduate): "Investigating the role of spatial structure on the origination of adaptive processes"
Grants and Fellowships
- NSF CISE Small Project Grant ($354,417 total, $84,086 to GVSU): Leveraging symbiotic co-evolution for improved problem-solving. Anya Vostinar (PI), Emily Dolson (Co-PI), and Alexander Lalejini (Co-PI). Submitted Fall 2023, work to be completed 2025 through 2027.
- GVSU Kindschi Fellowship ($3,500.00): Investigating the role of spatial structure on the origination of adaptive processes. John Shea and Alexander Lalejini (PI). Granted in Fall 2023, work done Winter 2024.
- GVSU Kindschi Fellowship ($3,500.00): Exploiting phylogenetic analysis to improve evolutionary search algorithms. Marcos Sanson and Alexander Lalejini (PI). Granted in Fall 2023, work done Winter 2024.
- GVSU Kindschi Fellowship ($4,000.00): Investigating the role of environmental connectivity on long-term evolutionary dynamics. Grant Gordon and Alexander Lalejini (PI). Granted in Summer 2024, work done Fall 2024.