This paper describes the use of genetic programming to automatically synthesize the design for a fixed-length loaded symmetric dipole antenna whose gain at a specific wavelength exceeds that of a commercially-marketed human-designed antenna and that reaches the theoretical maximum value for an antenna of its type. The run of genetic programming started "from scratch" that is, without starting from a pre-existing human-created design; did not employ any knowledge base of human design techniques or principles from the field of antenna design; and did not benefit from any human intervention during the run. The run produced a human-competitive result.

This paper has two aspects. First, it describes the use of genetic programming to automatically synthesize a solution to the challenge problem posed at an international competition held every four years in the field of optical design. In 2002, the competition at the International Optical Design Conference attracted 42 entries from 39 well-known optical designers, commercial consultants, and patent holders from many of the field's most prominent companies, universities, and research institutions. The 39 human contestants spent an average of 34.1 hours working on their entries. Virtually all entries were considered good solutions to the challenge problem. Genetic programming automatically synthesized a design "from scratch" - that is, without starting from a pre-existing human-created design and without pre-specifying the number of lenses, the physical layout of the lenses, or the numerical or non-numerical parameters of the lenses. The run of genetic programming did not employ any knowledge base of design techniques or principles from the field of optical design and did not entail any human intervention during the run. The genetically evolved optical lens system would have ranked in the middle (21st) if it had been entered into the 2002 competition and is therefore an instance of a "human-competitive" result produced by genetic programming. Second, this paper presents a mutation operation for numerical constants that is especially appropriate for problems in which the to-be-designed structure contains a large number of non-linearly interrelated numerical values and for problems in which the topology of the solution is to be automatically created.

This paper describes how genetic programming was used as an invention machine to automatically synthesize complete designs for six optical lens systems that duplicated the functionality of previously patented lens systems. The automatic synthesis was done "from scratch"-that is, without starting from a pre-existing good design and without pre-specifying the number of lenses, the physical layout of the lenses, the numerical parameters of the lenses, or the non-numerical parameters of the lenses. One of the six genetically evolved lens systems infringed a previously issued patent; three contained many of the essential features of the patents, without infringing; and the others were non-infringing novel designs that duplicated (or improved upon) the performance specifications contained in the patents. One of the six patents was issued in the 21st-century. The six designs were created in a substantially similar and routine way, suggesting that the approach used may have widespread utility. The genetically evolved designs are instances of human-competitive results produced by genetic programming in the field of optical design.

info:eu-repo/semantics/published

This paper describes how the massive parallelism of the rapidly reconfigurable Xilinx XC6216 FPGA (in conjunction with Virtual Computing's H.O.T. Works board) can be exploited to accelerate the time-consuming fitness measurement task of genetic algorithms and genetic programming. This acceleration is accomplished by embodying each individual of the evolving population into hardware in order to perform the fitness measurement task. A 16-step sorting network for seven items was evolved that has two fewer steps than the sorting network described in the 1962 O'Connor and Nelson patent on sorting networks (and the same number of steps as a 7-sorter that was devised by Floyd and Knuth subsequent to the patent and that is now known to be minimal). Other minimal sorters have been evolved.

Analog circuit synthesis entails the creation of both the topology and the sizing (numerical values) of all of the circuit's components. This paper presents a single uniform approach using genetic programming for the automatic synthesis of both the topology and sizing of a suite of eight different prototypical analog circuits, including a low-pass filter, a crossover filter, a source identification circuit, an amplifier, a computational circuit, a time-optimal controller circuit, a temperature-sensing circuit, and a voltage reference circuit. The problem-specific information required for each of the eight problems is minimal and consists of the number of inputs and outputs of the desired circuit, the types of available components, and a fitness measure that restates the high-level statement of the circuit's desired behavior as a measurable mathematical quantity. The eight genetically evolved circuits constitute an instance of an evolutionary computation technique producing results on a task that is usually thought of as requiring human intelligence.

April 01 1995 Hidden Order: How Adaptation Builds Complexity. John R. Koza John R. Koza Computer Science Department, 258 Gates Building, Stanford University, Stanford, CA 94305. [email protected], http://www-cs-faculty.stanford.edu/∼koza/ Search for other works by this author on: This Site Google Scholar Author and Article Information John R. Koza Computer Science Department, 258 Gates Building, Stanford University, Stanford, CA 94305. [email protected], http://www-cs-faculty.stanford.edu/∼koza/ Online Issn: 1530-9185 Print Issn: 1064-5462 © 1996 Massachusetts Institute of Technology1996 Artificial Life (1995) 2 (3): 333–335. https://doi.org/10.1162/artl.1995.2.3.333 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation John R. Koza; Hidden Order: How Adaptation Builds Complexity.. Artif Life 1995; 2 (3): 333–335. doi: https://doi.org/10.1162/artl.1995.2.3.333 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsArtificial Life Search Advanced Search This content is only available as a PDF. © 1996 Massachusetts Institute of Technology1996 Article PDF first page preview Close Modal You do not currently have access to this content.

This paper describes an automated process for the dynamic creation of a pattern-recognizing computer program consisting of initially-unknown detectors, an initially-unknown iterative calculation incorporating the as-yet-uncreated detectors, and an initially-unspecified final calculation incorporating the results of the as-yetuncreated iteration. The program's goal is to recognize a given protein segment as being a transmembrane domain or non-transmembrane area. The recognizing program to solve this problem will be evolved using the recentlydeveloped genetic programming paradigm. Genetic programming starts with a primordial ooze of randomly generated computer programs composed of available programmatic ingredients and then genetically breeds the population using the Darwinian principle of survival of the fittest and the genetic crossover (sexual recombination) operation. Automatic function definition enables genetic programming to dynamically create subroutines (detectors). When cross-validated, the best genetically-evolved recognizer achieves an out-of-sample correlation of 0.968 and an outof-sample error rate of 1.6%. This error rate is better than that recently reported for five other methods. 1. Statement of the Problem The goal in this paper is to use genetic programming with automatically defined functions (ADFs) to create a computer program for recognizing a given subsequence of amino acids in a protein as being a transmembrane domain or non-transmembrane area of the protein. The automated process that will create the recognizing program for this problem will be given a set of differently-sized protein segments and the correct classification for each segment. The recognizing program will consist of initiallyunspecified detectors, an initially-unspecified iterative calculation incorporating the as-yet-undiscovered detectors, and an initially-unspecified final calculation