Combinational multiple-valued circuit design by generalised disjunctive decomposition

A design of multiple-valued circuits based on the multiple-valued programmable logic arrays (MV PLA’s) by generalized disjunctive decomposition is presented. Main subjects are 1) Generalized disjunctive decomposition of multiple-valued functions using multiple-terminal multiplevalued decision diagrams (MTMDD’s); 2) Realization of functions by MV PLA-based combinatorial circuits

A probabilistic approach to analyse the evolutionary process in circuit design

One of the actual problems in the evolvable hardware is the evolvability of logic circuits. In order to understand better the nature of existing problem, the probabilistic analysis can be used. This paper aims to investigate how the circuit layout evolution is carried out. This is interesting thing to do for two main reasons. Firstly, to investigate what type of genes mostly influence on the algorithm performance in evolvable hardware. Secondly, to see how effective an allocation of active logic gates might be in a digital circuit design task. In order to achieve this goal we investigate the genotypes of the best chromosomes which bring some improvements in evolutionary process. The logic circuits have been evolved using circuit layout evolution

An extrinsic function-level evolvable hardware approach

The function level evolvable hardware approach to synthesize the combinational multiple-valued and binary logic functions is proposed in first time. The new representation of logic gate in extrinsic EHW allows us to describe behaviour of any multi-input multi-output logic function. The circuit is represented in the form of connections and functionalities of a rectangular array of building blocks. Each building block can implement primitive logic function or any multi-input multi-output logic function defined in advance. The method has been tested on evolving logic circuits using half adder, full adder and multiplier. The effectiveness of this approach is investigated for multiple-valued and binary arithmetical functions. For these functions either method appears to be much more efficient than similar approach with two-input one-output cell representation

Improving students motivation by means of multimedia

The student’s motivation is one of the most actual problems in the universities. One of many reasons for increased number of cases for low-level student motivation is that there are many more students in the higher education system with relatively low level of preparation. As a result, because of complex material delivered to students and their low ability to learn quickly, the motivation of students to learn programming and maths can be decreased significantly during educational process. In order to overcome this problem, the means of multimedia can be used in the educational process. The basic idea of proposed approach is to use the PowerPoint presentations, multimedia applications in the teaching process during both lectures and tutorials. The basic material should be accessible by students at any time. In order to do this, the material mentioned above should be published in Internet and intranet. The most complex material is implemented using a lot of animation in the lecture and tutorial material with relatively high frequency of repetition. It can be noted that in some cases the students become more motivated, once they understand the basic idea. The accessibility of material and its possibility to repeat it a lot of time have increased the level of student’s knowledge gained and have improved their motivation

Bidirectional incremental evolution in extrinsic evolvable hardware

Evolvable Hardware (EHW) has been proposed as a new technique to design complex systems. Often, complex systems turn out to be very difficult to evolve. The problem is that a general strategy is too difficult for the evolution process to discover directly. This paper proposes a new approach that performs incremental evolution in two directions: from complex system to sub-systems and from sub-systems back to complex system. In this approach, incremental evolution gradually decomposes a complex problem into some sub-tasks. In a second step, we gradually make the tasks more challenging and general. Our approach automatically discovers the sub-tasks, their sequence as well as circuit layout dimensions. Our method is tested in a digital circuit domain and compared to direct evolution. We show that our bidirectional incremental approach can handle more complex, harder tasks and evolve them more effectively, then direct evolution

Evolving more efficient digital circuits by allowing circuit layout evolution and multi-objective fitness

We use evolutionary search to design combinational logic circuits. The technique is based on evolving the functionality and connectivity of a rectangular array of logic cells whose dimension is defined by the circuit layout. The main idea of this approach is to improve quality of the circuits evolved by the GA by reducing the number of active gates used. We accomplish this by combining two ideas: 1) using multi-objective fitness function; 2) evolving circuit layout. It will be shown that using these two approaches allows us to increase the quality of evolved circuits. The circuits are evolved in two phases. Initially the genome fitness in given by the percentage of output bits that are correct. Once 100% functional circuits have been evolved, the number of gates actually used in the circuit is taken into account in the fitness function. This allows us to evolve circuits with 100% functionality and minimise the number of active gates in circuit structure. The population is initialised with heterogeneous circuit layouts and the circuit layout is allowed to vary during the evolutionary process. Evolving the circuit layout together with the function is one of the distinctive features of proposed approach. The experimental results show that allowing the circuit layout to be flexible is useful when we want to evolve circuits with the smallest number of gates used. We find that it is better to use a fixed circuit layout when the objective is to achieve the highest number of 100% functional circuits. The two-fitness strategy is most effective when we allow a large number of generations

The effect of missing values using genetic programming on evolvable diagnosis

Medical databases usually contain missing values due the policy of reducing stress and harm to the patient. In practice missing values has been a problem mainly due to the necessity to evaluate mathematical equations obtained by genetic programming. The solution to this problem is to use fill in methods to estimate the missing values. This paper analyses three fill in methods: (1) attribute means, (2) conditional means, and (3) random number generation. The methods are evaluated using sensitivity, specificity, and entropy to explain the exchange in knowledge of the results. The results are illustrated based on the breast cancer database. Conditional means produced the best fill in experimental results

A New Evolutionary Hardware Approach for Logic Design.

This poster paper summarizes ongoing dissertation research defining an evolvable hardware methodology for evolving combinational binary and multiple-valued logic circuits. This dissertation provides an overview of current evolvable hardware approaches; defines the combinational logic design problem; describes the gate and function level evolvable hardware technique; and develops a new methodology for evolving binary and multiple-valued combinational logic circuits with and without automatically defined functions. The new methodology promises significant improvements over current conventional algebraic techniques

Multi-colony ant systems for multi-hose routing

This article is available open access through the publisher’s website at the link below. Copyright @ 2012 International Journal of Computer Applications.Ant System (AS) is a general purpose heuristic algorithm inspired by the foraging behaviour of real ant colonies. AS and its improved versions have been successfully applied to difficult combinatorial optimization problems such as travelling salesman problem, quadratic assignment problem and job shop scheduling. In this paper, two versions of multi-colony ant systems that are extensions to the AS are proposed for the multi-hose routing. In both versions, each colony of ants searches for an optimum path between two end points (or commodities). While each colony searches for optimum paths, they try to maximum use of other colonies paths (sharing paths, or bundling) for easy handling of multiple paths. The first version uses a single pheromone matrix for all colonies and the second version uses different pheromone matrices for each colony and a modified random propositional rule to attract ants toward foreign pheromones. The tessellated format of the obstacles was used in the algorithm instead of the original shapes of the obstacles. As a result of using this format, the algorithm can handle freeform obstacles and speed up the algorithm when checking the collision detections. The experimental results show that there is no significant difference in the quality of the solutions produced by two versions and the first version takes less computation time. Further first version needs low computer memory and one parameter lesser than of the second version

Risk evaluation using evolvable discriminate function

This essay proposes a new approach to risk evaluation using disease mathematical modeling. The mathematical model is an algebraic equation of the available database attributes and is used to evaluate the patient condition. If its value is greater than zero it means that the patient is ill (or in risk condition), otherwise healthy. In practice risk evaluation has been a very difficult problem mainly due its sporadic behavior (suddenly, the patient has a stroke, etc as a condition aggravation) and its database representation. The database contains, under the label of risk patient data, information of the patient condition that sometimes is in risk condition and sometimes is not, introducing errors in the algorithm training. The study was applied to Atherosclerosis database from Discovery Challenge 2003 - ECML/PKDD 2003 workshop