The goal of genetic programming (GP), a subfield of evolutionary computing, is the automatic development of solutions to problems.
Since its inception in the early 1990s, GP has emerged as one of the most promising paradigms for problem-solving in the field of artificial intelligence, yielding a number of outcomes that are competitive with those of humans and even new innovations that may be patented. Additionally, GP continues to develop swiftly, with new concepts, methods, and applications being suggested continuously, just like other areas of computer science.
The goal of this book is to demonstrate recent developments in the subject of GP, including the creation of new theoretical paradigms and implementations that have successfully addressed a variety of challenges in the real world. Although it is hoped that undergraduates who are interested in learning about the most cutting-edge GP approaches would find it interesting, the volume is primarily intended for postgraduates, researchers, and educators.
