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A Tunable Deceptive Problem to Challenge Genetic and Evolutionary Computation and Other A.I.
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A significant challenge in intelligence analysis involves knowing when a social network description is ‘complete’, i.e., when sufficient connections have been found to render the network complete. In this paper, a combination of methods is used to predict covert network structures for specific missions. The intention is to support hypothesis-generation in the Social Network Analysis of covert organisations. The project employs a four phase approach to modelling social networks, working from task descriptions rather than from contacts between individual: phase one involves the collation of intelligence covering types of mission, in terms of actors and goals; phase two involves the building of task models, based on Cognitive Work Analysis, to provide both a process model of the operation and an indication of the constraints under which the operation will be performed; phase three involves the generation of alternative networks using Genetic Programming; phase four involves the analysis of the resulting networks using social network analysis. Subsequent analysis explores the resilience of the networks, in terms of their resistance to losses of agents or tasks. The project demonstrates that it is possible to define a set of structures that can be tackled using different intervention strategies, demonstrates how patterns of social network structures can be predicted on the basis of task knowledge, and how these structures can be used to guide the gathering of intelligence and to define plausible Covert Networks
A method is presented for evolving individuals that use an Attribute Grammar (AG) in a generative way. AGs are considerably more flexible and powerful than the closed, context free grammars normally employed by GP. Rather than evolving derivation trees as in most approaches, we employ a two step process that first generates a vector of real numbers using standard GP, before using the vector to produce a parse tree. As the parse tree is being produced, the choices in the grammar depend on the attributes being input to the current node of the parse tree. The motivation is automatic parallelization or the discovery of a re-factoring of a sequential code or equivalent parallel code that satisfies certain performance gains when implemented on a target parallel computing platform such as a multicore processor. An illustrative and a computed example demonstrate this methodology.
This Genetic Programming based tool simulates activities and resource allocations in the Program (Project) Evaluation and Review Technique method of project control. Users constrain the optimization problem by means of a visual interface and Genetic Programming discovers a umber of acceptable solutions that satisfy the user constraints. It evolves computer programs that, when executed, produce a variable length vector of real numbers. This vector is then interpreted according to the grammar that abides by the user constraints. The tool has a wide application in the management of large and complex projects as it handles the a priori simulation of events that may delay or compromise the project, and enables the project owners and project managers to come up with robust and innovative contingency measures to decrease the likelihood of project failure before project start-up.
. We describe a Genetic Algorithm that can evolve complete programs. Using a variable length linear genome to govern how a Backus Naur Form grammar definition is mapped to a program, expressions and programs of arbitrary complexity may be evolved. Other automatic programming methods are described, before our system, Grammatical Evolution, is applied to a symbolic regression problem. 1 Introduction Evolutionary Algorithms have been used with much success for the automatic generation of programs. In particular, Koza's [Koza 92] Genetic Programming has enjoyed considerable popularity and widespread use. Koza's method originally employed Lisp as its target language, and others still generate Lisp code. However, most experimenters generate a homegrown language, peculiar to their particular problem. Many other approaches to automatic program generation using Evolutionary Algorithms have also used Lisp as their target language. Lisp enjoys much popularity for a number of reasons, not least...
In tree-based genetic programming (GP), the most frequent subtrees on later generations are likely to constitute useful partial
solutions. This paper investigates the effect of encapsulating such subtrees by representing them as atoms in the terminal
set, so that the subtree evaluations can be exploited as terminal data. The encapsulation scheme is compared against a second
scheme which depends on random subtree selection. Empirical results show that both schemes improve upon standard GP.
Genetic Programming solution of the convection-diffusion equation
- howard
paper 15 - Predicting the structure of covert networks using Genetic Programmingm
- C Baber
- N Stanton
- D Howard
- R J Houghton