Sequential algorithms as bistable maps
نویسنده
چکیده
We exhibit Cartwright-Curien-Felleisen’s model of observably sequential algorithms as a full subcategory of Laird’s bistable biorders, thereby reconciling two views of functions: functions-as-algorithms (or programs), and functions-as-relations. We then characterize affine sequential algorithms as affine bistable functions in the full subcategory of locally boolean orders.
منابع مشابه
Locally Boolean Domains and Universal Models for Infinitary Sequential Languages
In the first part of this Thesis we develop the theory of locally boolean domains and bistable maps (as introduced in [Lai05b]) and show that the category of locally boolean domains and bistable maps is equivalent to the category of Curien-Lamarche games and observably sequential functions (cf. [CCF94]). Further we show that the category of locally boolean domains has inverse limits of ω-chains...
متن کاملBistable Biorders: A Sequential Domain Theory
We give a simple order-theoretic construction of a Cartesian closed category of sequential functions. It is based on bistable biorders, which are sets with a partial order — the extensional order — and a bistable coherence, which captures equivalence of program behaviour, up to permutation of top (error) and bottom (divergence). We show that monotone and bistable functions (which are required t...
متن کاملSequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR
Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...
متن کاملSequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR
Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...
متن کاملDetermination of Best Supervised Classification Algorithm for Land Use Maps using Satellite Images (Case Study: Baft, Kerman Province, Iran)
According to the fundamental goal of remote sensing technology, the image classification of desired sensors can be introduced as the most important part of satellite image interpretation. There exist various algorithms in relation to the supervised land use classification that the most pertinent one should be determined. Therefore, this study has been conducted to determine the best and most su...
متن کامل