Tag: Soft Computing – A Fusion of Foundations, Methodologies and Applications

Abstract  Bounded residuated lattice ordered monoids (

Rl

-monoids) are a common generalization of pseudo-

BL

-algebras and Heyting algebras, i.e. algebras of the non-commutative basic fuzzy logic (and consequently of the basic fuzzy
logic, the Łukasiewicz logic and the non-commutative Łukasiewicz logic) and the intuitionistic logic, respectively. We investigate
bounded

Rl

-monoids satisfying the general comparability condition in connection with their states (analogues of probability measures).
It is shown that if an extremal state on Boolean elements fulfils a simple condition, then it can be uniquely extended to
an extremal state on the

Rl

-monoid, and that if every extremal state satisfies this condition, then the

Rl

-monoid is a pseudo-

BL

-algebra.

Abstract  Stock selection for hedge fund portfolios is a challenging problem for Genetic Programming (GP) because the markets (the environment
in which the GP solution must survive) are dynamic, unpredictable and unforgiving. How can GP be improved so that solutions
are produced that are robust to non-trivial changes in the environment? We explore two new approaches. The first approach uses subsets of extreme environments
during training and the second approach uses a voting committee of GP individuals with differing phenotypic behaviour.

Abstract  This paper combines a powerful algorithm, called Dongguang Li (DGL) global optimization, with the methods of cancer diagnosis
through gene selection and microarray analysis. A generic approach to cancer classification based on gene expression monitoring
by DNA microarrays is proposed and applied to two test cancer cases, colon and leukemia. The study attempts to analyze multiple
sets of genes simultaneously, for an overall global solution to the gene’s joint discriminative ability in assigning tumors
to known classes. With the workable concepts and methodologies described here an accurate classification of the type and seriousness
of cancer can be made. Using the orthogonal arrays for sampling and a search space reduction process, a computer program has
been written that can operate on a personal laptop computer. Both the colon cancer and the leukemia microarray data can be
classified 100% correctly without previous knowledge of their classes. The classification processes are automated after the
gene expression data being inputted. Instead of examining a single gene at a time, the DGL method can find the global optimum
solutions and construct a multi-subsets pyramidal hierarchy class predictor containing up to 23 gene subsets based on a given
microarray gene expression data collection within a period of several hours. An automatically derived class predictor makes
the reliable cancer classification and accurate tumor diagnosis in clinical practice possible.