Massively-concurrent Agent-based Evolutionary Computing
This work addresses a technological bottleneck in evolutionary computation for researchers and practitioners, though it is incremental as it builds on existing EMAS concepts.
The authors tackled the problem of implementing fully asynchronous agents in evolutionary multi-agent systems (EMAS) by developing a new algorithm using functional languages like Erlang and Scala, resulting in faster and more efficient solutions for common optimization problems.
The fusion of the multi-agent paradigm with evolutionary computation yielded promising results in many optimization problems. Evolutionary multi-agent system (EMAS) are more similar to biological evolution than classical evolutionary algorithms. However, technological limitations prevented the use of fully asynchronous agents in previous EMAS implementations. In this paper we present a new algorithm for agent-based evolutionary computations. The individuals are represented as fully autonomous and asynchronous agents. An efficient implementation of this algorithm was possible through the use of modern technologies based on functional languages (namely Erlang and Scala), which natively support lightweight processes and asynchronous communication. Our experiments show that such an asynchronous approach is both faster and more efficient in solving common optimization problems.