Evolving Digital Circuits for the Knapsack Problem
Mihai Oltean, Crina Gro\c{s}an, Mihaela Oltean
TL;DR
This paper demonstrates how Multi Expression Programming, a genetic programming technique, can evolve digital circuits to effectively solve the NP-Complete knapsack problem, showing promising experimental results.
Contribution
It introduces the application of Multi Expression Programming to evolve digital circuits specifically for the knapsack problem, highlighting its effectiveness.
Findings
MEP performs well on test problems
Digital circuits evolved by MEP solve the knapsack problem efficiently
Experimental results support MEP's applicability to NP-Complete problems
Abstract
Multi Expression Programming (MEP) is a Genetic Programming variant that uses linear chromosomes for solution encoding. A unique feature of MEP is its ability of encoding multiple solutions of a problem in a single chromosome. In this paper we use Multi Expression Programming for evolving digital circuits for a well-known NP-Complete problem: the knapsack (subset sum) problem. Numerical experiments show that Multi Expression Programming performs well on the considered test problems.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEvolutionary Algorithms and Applications · Metaheuristic Optimization Algorithms Research · Scheduling and Optimization Algorithms