Quantum — Inspired evolutionary algorithms for solution of large scale optimization problems

Summary form only given. Evolutionary Algorithms have emerged as strong candidates for the solution of large scale optimization problems. Their multi-point search capabilities are especially suited for parallelization on the modern computing machines. They have been utilized in many domains and success stories abound in the literature. Quantum Evolutionary Algorithms (QEA) is a recent branch of EAs. QEA is a population-based probabilistic Evolutionary Algorithm that integrates concepts from quantum computing for higher representation power and robust search. QEAs are characterized by population dynamics, individual representation, evaluation function etc., as in EAs, as well as quantum bit (qubit) representation, superposition of states etc. as in Quantum Computing. The advantage of the QEAs is that, unlike the other EAs, they can work with small population sizes without being stuck in local minima and without converging prematurely because of loss of diversity. In the extreme case, the immense representation power of the qubits enables use of population size of 1. This reduces the computational burden and enables the solution of large sized problems. The talk would introduce EAs and QEAs and present some of our recent work on QEAs and applications.