Bio-inspired Computing Based Key Technology Researches And Implementations Of Knowledge Discovery

Bio-inspired Computing is the intelligence-computing model enlightened by natural intelligence and biological processes. In recent years, Bio-inspired Computing has been applied widely in many fields, such as artificial intelligence, machine learning and data mining. Enlightened from new achieves in biological science, computer scientists have proposed many new theories and algorithms of bio-inspired computing while it is still in the beginning stage comparing with other elder science fields.This dissertation has explored some interesting and challenging issues in bio-inspired computing. The main contributions include:1. Proposes a new evolution algorithm: M-GEP (Multi-Layer Chromosome Gene Expression Programming) based on the new concept named multi-layer chromosomes. The algorithm is efficient in real applications, such as function discovery, electronic circuit evolution, etc. Extensive experiments on the traditional single gene and multi-genes GEP show that the average number of generations of M-GEP is reduced to 29%～ 81%.2. Proposes and implements a new evolution algorithm- MEOE (multi-gene evolutionary algorithm based on overlapped expression). Biologists find that nucleoside sequence, which forms the genes, can be overlapped as overlapping gene in specific conditions. Biologists believe that overlapping gene can save material and energy to store genetic information; moreover, it can adjust the expression of genes. Enlightened by overlapping gene and concept of thickness in natural immune system, the dissertation proposes and implements a new evolution algorithm-MEOE.