Usage Usage of of Genetic Genetic Algorithm Algorithm for for Lattice Lattice Drawing Drawing Sahail Owais, Petr Gajdoš, Václav Snášel Suhail Owais, Petr Gajdoš and Václav Snášel Department of Computer Science, Department VŠB - TechnicalofUniversity ComputerofScience, Ostrava, tř.V17. ŠBlistopadu - Technical 15,University of Ostrava, 708 33 Ostrava-Poruba tř. 17. listopadu 15, 708 Czech 33 Ostrava-Poruba Republic Czech Republic Suhail.Owais@vsb.cz Suhail.Owais@vsb.cz Petr.Gajdos@vsb.cz Petr.Gajdos@vsb.cz Vaclav.Snasel@vsb.cz Vaclav.Snasel@vsb.cz Abstract. Lattice diagrams, known as Hasse diagrams, have played an ever increasing role in lattice theory and fields that use lattices as a tool. Initially regarded with suspicion, they now play an important role in both pure lattice theory and in data representation. Using evolutionary algorithms-genetic algorithms for drawing a lattice diagram is the main goal of our research. It depends on stochastic methods on searching of concepts’ positions, with trying to draw it with less number of intersec- tions. Keywords: FCA, Genetic Algorithm, Hasse Diagram 1 Graphic representation of concept lattice These Hasse diagrams are an important tool for researchers in lattice theory and ordered set theory and are now used to visualize data. This paper also shows a new approach how to draw a Hasse diagrams using genetic algorithm. In the following paragraphs we would like to mention known approaches of lattice drawing and main points which make a Hasse diagrams or random graphs more readable. After that we explain our approach and all important terms from the area of genetic algorithms. An ordered set P = (P, ≤) consists of a set P and a partial order relation ≤ on P . That is, the relation ≤ is reflexive (x ≤ x), transitive (x ≤ y and y ≤ z ⇒ x ≤ z) and antisymmetric (x ≤ y and y ≤ x ⇒ x = y). If P is finite there is a unique smallest relation ≺, known as the cover or neighbour relation, whose transitive, reflexive closure is ≤. (Graph theorists call this the transitive reduct of ≤.) A Hasse diagram of P is a diagram of the acyclic graph (P, ≺) where the edges are straight line segments and, if a < b in P, then the vertical coordinate for a is less than the one for b. Because of this second condition arrows are omitted from the edges in the diagram. A lattice is an ordered set in which every pair of elements a and b has a least upper bound, a ∨ b, and a greatest lower bound, a ∧ b, and so also has a Hasse diagram. Radim Bělohlávek, Václav Snášel (Eds.): CLA 2005, pp. 82–91, ISBN 80–248–0863–3. Usage of Genetic Algorithm for Lattice Drawing 83 1.1 Known approaches of lattice drawing By the 1970s diagrams had become an important tool in lattice theory. There exist some known methods how to draw the Hasse diagrams. The first one it called Hierarchical Layout Partially ordered sets, for the purposes of layout, are seen as hierarchies. An annotated bibliography by Battista et. [1] describes the approach as A hierarchical drawing of an acyclic diagram is an upwards polyline drawing where the vertices and bends are constrained to lie on a set of equally spaced horizontal lines. Sugiyamas algorithm is typical of a variety of algorithms proposed for the layout of such diagrams. There are three main steps of whole process. 1. Partition the vertices into layers. 2. Reduce the number of edge crossings by swapping the horizontal ordering of vertices. 3. Minimize the number of bends, where bends are dummy nodes inserted to ensure that lines travel only between adjacent layers. Next there are additive or non additive diagrams and the methods to draw Hasse diagrams which were introduced by Rudolf Willw [11]. But we can men- tion the main points of each drawing. These are the most important points to draw a readable graph: – Avoid crossing of lines. – Try to draw parallel lines. – Identify known structures: cubes, rectangles . . . – Layer the nodes: draw nodes on the same layer, if their concept’s extents have the same size. – Try to draw steep lines, avoid flat ones. 2 Evolutionary Algorithms Evolutionary algorithms are stochastic search methods that mimic the metaphor of natural biological evolution, which applies the principles of evolution found in nature to the problem of finding an optimal solution to a solver problem. An evolutionary algorithm is a generic term used to indicate any population-based optimization algorithm that uses mechanisms inspired by biological evolution, such as reproduction, mutation and recombination. Candidate solutions to the optimization problem play the role of individuals in a population, and the cost function determines the environment within which the solutions ”live” (see figure 1. Evolutionary Algorithms Evolutionary algorithms are stochastic search methods that mimic the metaphor of natural biological evolution, which applies the principles of evolution found in nature to 84 Suhail the problemOwais, Petr of finding Gajdoš, an optimal Václav solution Snášel to a solver problem. An evolutionary algorithm is a generic term used to indicate any population-based optimization algorithm that uses mechanisms inspired by biological evolution, such as reproduction, mutation and recombination. Candidate solutions to the optimization 1). Evolution of the problem play population the role of individualsthen takes place in a population, and theafter the repeated cost function determines theapplication of the above operators. environment within which Figure 1 shows the solutions theEvolution "live" [1]. structure of the of a simple population evolutionary then takes placefor algorithm aftersolving the repeated application ofGenetic a problem. the above algorithm operators. Figure 1 shows is the mostthe structure popular type of of a simple evolutionary algorithm for solving a problem. Genetic algorithm is the most evolutionary algorithms. popular type of evolutionary algorithms. fitness assign- selection ment Problem Coding of solutions Evolutionary Objective function Search Solution Evolutionary operators Recom- mutation bination Fig-1: Problem Solution Using Evolutionary Algorithm. Fig. 1. Basic scheme of our solution. 2. Genetic Algorithms (GA) Genetic algorithms (GA) described by John Holland in 1960s and further developed by Holland and his students and colleagues at the University of Michigan in 3 Genetic Algorithms (GA) the 1960s and 1970s [2]. GA used Darwinian Evolution to extract nature optimization strategies that use them successfully and transform them for application in mathematical Geneticoptimization algorithms (GA) theory to finddescribed by John the global optimum Holland in defined in 1960s phase space and further devel- [3, 4, 5]. oped by HollandGA isand usedhis students to extract and colleagues approximate atproblems solutions for the University through a ofset Michigan of in operations “fitness function, selection, crossover, and mutation”. Such operators are the 1960s and 1970s. GA used Darwinian Evolution to extract principles of evolutionary biology applied to computer science. GA search process nature optimiza- tion strategies that use them successfully and transform them for application in mathematical optimization theory to find the global optimum in defined phase space [4][5][6]. GA is used to extract approximate solutions for problems through a set of operations ”fitness function, selection, crossover, and mutation”. Such operators are principles of evolutionary biology applied to computer science. GA search process depends on different mechanisms such as adaptive methods, stochastic search methods, and use probability for search. Using GA for solving most difficult problems that searches for accepted so- lution; where this solution may not be the best and the optimal one for the problem. GA are useful for solving real and difficult problems, adaptive and op- timization problems, and for modeling the natural system that inspired design [9][2]. Some applications that can be solved by GA are: Scheduling ’Job, and Trans- portation Scheduling’, Design ’Communication Network design’, Control ’Gas pipeline control’, Machine Learning ’Designing Neural Networks’, Robotics ’Path planning’, Combinatorial Optimization ’TSP, Graph Bisection, and Routing’, Signal Processing ’Filter Design’, Image Processing ’Pattern recognition’, Busi- ness ’Evaluating credit risks’, and in Medical ’Studying health risks for a popu- lation exposed to toxins’ [10]. Usage of Genetic Algorithm for Lattice Drawing 85 4 Genetic Algorithms Operators Typically, any genetic algorithm used for purpose of optimization consists of the following features: 1. Chromosome or individual representation. 2. Objective function “fitness function”. 3. Genetic operators (selection, crossover and mutation). Where applying GA done over a population of individuals or chromosomes shows that several operators are utilized. Presenting of GA process described in a flowchart shown in 2. 4.1 Chromosome Encoding Encoding by simple; representation of integer numbers by binary encoding. Rep- resentation for chromosome (suggested solution) will be in a string of symbols to simplify using of GA operators and to help in reproduction of optimal solution. Encoding may be in string of bits, or in arrays, or in lists, or in tree structure, and may be in other types. Some examples were shown in table 1. Encoding Type Example Binary Encoding 1101100100110110 Permutation Encoding 8 5 6 7 2 3 1 4 9 Value Encoding (back), (back), (right), (left) Table 1. Examples for encoding types 4.2 Objective Function The goal of GA is to find a solution to a complex optimization problem, which optimal or near-optimal. GA searches for better performing candidates, where performance can be measured in terms of objective”fitness” function. The ob- jective is to choose the proportion of financial assets to hold in a portfolio such that risk is minimized given the constraint of achieving a specified level of re- turn. Fitness function does as a metrics to measure scheduler performance for each chromosome in the problem. 4.3 Selection Operator Selection determines which solution candidates are allowed to participate in crossover and undergo possible mutation. Select two chromosomes with the high- est quality values from the population using one of the selection methods. Some of these methods are: Elitism Selection, Roulette Wheel Selection, Tournament Selection, Rank Selection, and Stochastic Selection. 86 Suhail Owais, Petr Gajdoš, Václav Snášel 4.4 Crossover operator Promising candidates, as represented by relatively better performing solutions, are combined through a process of binary recombination referred to as crossover. This ensures that the search process is not random but rather that it is con- sciously directed into promising regions of the solution space. Crossover ex- changes subparts of the selected chromosomes, where the position of the subparts selected randomly to produce offsprings. Some of crossover methods types are: Single Point Crossover, Two Points Crossover, Multipoint Crossover, Uniform Crossover, and Arithmetic Crossover. 4.5 Mutation operator New genetic material can be introduced into the population through mutation. This increases the diversity in the population. Mutation occurs by randomly selecting particular elements in a particular offsprings. GA process terminated by satisfies one of terminating conditions often in- clude: – Fixed number of generations reached. – Budgeting: allocated computation time/money used up. – An individual is found that satisfies minimum criteria. – The highest ranking individual’s fitness is reaching or has reached a plateau such that successive iterations are not producing better results anymore. – Manual inspection. May require start-and-stop ability. – Combinations of the above. 5 Genetic algorithm implementation In the this chapter we try to describe our approach to draw a Hasse diagram for a concept lattice using genetic algorithm. There are five levels and ten concepts (nodes) in the figure 3. In the first generation the population of a chromosomes were generated randomly. At the beginning the number of levels is computed ac- cording to an incremental algorithm made by Petko Valtchev. Then we compute the level position for each node, some nodes may appear in different levels, e.g. the concept number six could be moved one level up. Next vector (see table 2) consists of all edges in the Hasse diagram. The numbers represent the indexes of nodes (concepts). From 0 0 0 5 5 8 8 6 6 9 9 7 4 2 To 6 5 9 3 4 3 9 4 2 7 2 1 1 1 Table 2. Vector of diagram edges Usage of Genetic Algorithm for Lattice Drawing 87 Start Generate Initial Population Encoding generated population Evaluate Fitness Functions R Best E Yes individuals Meets G Optimization E Criteria? N E R Stop A Selection (select parents) T I O Crossover (married parents) N Mutation (mutate offsprings) Fig-2: Flowchart for Genetic Algorithm Fig. 2. asdadadad 0 Level 4 8 5 Level 3 9 6 3 Level 2 2 7 4 Level 1 Level 0 1 Fig. 3. Hasse digram with marked levels. 5.1 Chromosome encoding Each chromosome was encoded as a vector which consists of x and y coordi- nates for all nodes in the graph. The coordinates will be generated randomly 88 Suhail Owais, Petr Gajdoš, Václav Snášel for node with respect to the levels of nodes. Table 3 shows an example for such chromosome. X-coord. 200 200 50 250 300 270 180 200 100 70 Y-coord. 50 400 300 200 300 100 200 300 100 200 Table 3. Chromosome encoding 6 Fitness function We are looking for more readable graph. The most important constrains we look for the minimal number of edges’ intersections. 6.1 Selection method The best two chromosomes will be selected depends on the minimal number of intersections and called parents. They will be used in the other operators crossover and mutation to produce two new offspring. 6.2 Crossover Three types of crossover will be implemented (single point, two points and multi points crossover) over selected parents. Following figures show examples for the three types. For the single point crossover (see figure 4) the one node position will be randomly chosen. It will divide the parents’ chromosomes into two parts. Then first offspring’s chromosome consists of the first part from the first parent and second part from the second parent. The second offspring consists of the first 8 part from the second parent and the second part from the first parent. 9 6 Offspring 1 X 200 200 50 250 300 70 250 400 300 200 2 7 Parent 1 X 200 200 50 250 300 270 180 200 100 70 Y 50 400 300 200 300 100 100 300 100 200 Y 50 400 300 200 300 100 200 300 100 200 X 200 200 100 50 250 70 250 400 300 200 Offspring 2 Parent 2 Y 50 400 300 200 300 100 100 300 100 200 X 200 200 100 50 250 270 180 200 100 70 Y 50 400 300 200 300 100 200 300 100 200 Fig. 4. Single point crossover. Offspring 1 X 200 200 100 250 300 270 180 400 300 200 Y 50 400 300 200 300 100 200 300 100 200 Parent 1 X 200 200 50 250 300 270 180 200 100 70 For theY two 50 400 300 200 300 100 200 300 100 200 points crossover (see figure 5) two nodes’ positions will be ran- domly chosen. X 200 This 200 100will divide 50 250 the 70 250 400 parents’ 300 200 chromosomes Offspring 2 into three parts. Then Parent 2 Y 50 400 300 200 300 100 100 300 100 200 X 200 200 50 50 250 70 250 200 100 70 Y 50 400 300 200 300 100 100 300 100 200 Offspring 1 X 200 200 100 250 250 70 180 400 100 200 X 200 200 50 250 300 270 180 200 100 70 Y 50 400 300 200 300 100 200 300 100 200 Parent 1 Y 50 400 300 200 300 100 200 300 100 200 X 200 200 100 50 250 70 250 400 300 200 Offspring 2 Parent 2 Y 50 400 300 200 300 100 100 300 100 200 X 200 200 50 50 300 270 250 200 300 70 Y 50 400 300 200 300 100 100 300 100 200 8 9 6 Usage of Genetic Algorithm Offspring 1 for Lattice Drawing 89 X 200 200 50 250 300 70 250 400 300 200 2 7 Parent 1 X 200 200 50 250 300 270 180 200 100 70 Y 50 400 300 200 300 100 100 300 100 200 Y 50 400 300 200 300 100 200 300 100 200 first offspring’s chromosome consists of the first and third parts from the first parent Parent 2 and X second 200 200 100part from 50 250 the400second 70 250 300 200 parent. The Offspring 2 second offspring consists Y 50 400 300 200 300 100 100 300 100 200 X 200 200 100 50 250 270 180 200 100 70 8 of the second part from the first parent and the Y 50 400 300 200third first and 300 100parts 200 300 from 100 200the second parent. 9 6 Offspring 1 X 200 200 50 250 300 70 250 400 300 200 Offspring 2 7 Parent 1 X 200 200 50 250 300 270 180 200 100 70 Y 50 1 400 300 200 300 100 100 300 100 200 Y 50 400 300 200 300 100 200 300 100 200 X 200 200 100 250 300 270 180 400 300 200 Y 50 400 300 200 300 100 200 300 100 200 Parent 1 X 200 200 50 250 300 270 180 200 100 70 Y X 50 200 200 300 200 400 100 50 300 250 100 250 300 70 200 400 100 200 300 200 Parent 2 Offspring 2 Y 50 400 300 200 300 100 100 300 100 200 X 200 200 100 50 250 270 180 200 100 70 X 200 200 100 50 250 70 250 400 300 200 Y 50 2 400 300 200 300 100 200 300 100 200 Offspring Parent 2 Y 50 400 300 200 300 100 100 300 100 200 X 200 200 50 50 250 70 250 200 100 70 Y 50 400 300 200 300 100 100 300 100 200 Fig. 5. Two points crossover. Offspring 1 X 200 200 100 250 300 270 180 400 300 200 Offspring Y 50 1 400 300 200 300 100 200 300 100 200 Parent 1 X 200 200 50 250 300 270 180 200 100 70 X 200 200 100 250 250 70 180 400 100 200 Y X 200 50 200 200 300 50 400 300 270 250 300 100 180 300 100 200 200 100 200 70 Y 50 400 300 200 300 100 200 300 100 200 Parent 1 Y 50 400 300 200 300 100 200 300 100 200 For theX multi points crossover (see figure 6) Parent 2 200 200 100 50 250 70 250 400 300 200 random Offspring 2 number n of nodes will Y 50 200 300 200 400 100 50 300 250 100 250 300 70 100 400 100 200 X 2002 200 50 50 250 70 250 200 100 70 Offspring be Parent chosen. 2 X Also we generate n number of nodes’ Y 200 300 200 50 400 300 200 300 100 100 300 100 200 Y 200 X positions 50 200 400 300 50 200 randomly 100 100 50 300 270 to 300 100 250 200 swap 300 200 70 these nodes to produce two new offspring. Y 50 400 300 200 300 100 100 300 100 200 Offspring 1 X 200 200 100 250 250 70 180 400 100 200 Offspring before mutation Offspring after mutation X 200 200 50 250 300 270 180 200 100 70 Y 50 400 300 200 300 100 200 300 100 200 Parent X 200 1 200 50 50 250 70 250 200 100 70 X 200 200 50 50 220 70 250 200 100 70 Y 50 400 300 200 300 100 200 300 100 200 Y 50 400 300 200 300 100 100 300 100 200 Y 50 400 300 200 300 100 100 300 100 200 0.4 0.7 0.1 0.9 0.6 0.3 0.3 0.5 X 200 200 100 50 250 70 250 400 300 200 Offspring 2 Parent 2 220 Y 50 400 300 200 300 100 100 300 100 200 X 200 200 50 50 300 270 250 200 300 70 Mutation value = 0.2 Y 50 400 300 200 300 100 100 300 100 200 Fig. 6. Multi points crossover. 0 0 Offspring before mutation Offspring after mutation X 200 200 50 50 250 70 250 200Level 4 100 70 X 200 200 50 50 220 70 250 200 100 Level 70 4 Y 50 400 300 200 3005 100 100 300 100 200 Y 50 400 300 200 300 100 5 100 300 100 200 8 8 0.4 0.7 0.1 0.9 0.6 0.3 0.3 0.5 6 Level 3 Level 3 220 9 6 9 6.3 Mutation Mutation value = 0.2 3 Level 2 3 Level 2 The two2 new offspring 4 will be mutated randomly to2 change some4x coordinates Level 1 Level 1 7 7 depends on mutation value. For each node the random value will be selected and if this value0 is less thenLevel mutation 0 value then new random0 number Level will 0be 1 Level 4 1 Level 4 generated to define x coordinate of the node. Figure 7 shows an example. The 8 5 8 5 x coordinate of the node number Level 3 4 was mutated from the value 250 to the 6 Level220 3 because9 the random 6 number 0.1 for this node is less 9then the mutation value0.2. The Nodes number 3zero and Level one will 2 be not mutated, because they3 will beLevel placed 2 in the middle of the x − axes (see figure 7). Some nodes may be mutated by 2 7 4 Level 1 2 7 4 Level 1 change its level (y − coordinate) depends on the position of parent concept and child concept. If there is a Level free 0level between selected concept and its Level parent 0 concept, then it1 can be mutated. Correspondingly, if there is1 a free level between selected concept and its child concept. See figure 8. Y 50 400 300 200 300 100 100 300 100 200 200 200 XOffspring 1 100 50 250 270 180 200 100 70 Y X 50200 200300 400 100200250 300 100 300 270 400 180300 200 100300200 200 Y 50 400 300 200 300 100 200 300 100 200 Parent 1 X 200 200 50 250 300 270 180 200 100 70 Y 50 400 300 200 300 100 200 300 100 200 X 200 200 100 50 250 70 250 400 300 200 Offspring 2 Parent 2 Y 50 400 300 200 300 100 100 300 100 200 Offspring X 200 1 200 50 50 250 70 250 200 100 70 X Y 20050200 100300250 400 200300 270 300 100400 180 100 300300 100200200 Y 50 400 300 200 300 100 200 300 100 200 Parent 1 X 200 200 50 250 300 270 180 200 100 70 Y 50 400 300 200 300 100 200 300 100 200 90 X 200 200 100 50Petr 70 250 400 Václav 250 Gajdoš, 300 200 Snášel Parent 2 Suhail Owais, Offspring 2 Offspring 1 Y 50 400 300 200 300 100 100 300 100 200 X 200 200 50 50 250 70 250 200 100 70 X 200 200 100 250 250 70 180 400 100 200 Y 50 400 300 200 300 100 100 300 100 200 X 200 200 50 250 300 270 180 200 100 70 Y 50 400 300 200 300 100 200 300 100 200 Parent 1 Y 50 400 300 200 300 100 200 300 100 200 then fitness values will be computed for the new offspring. These offspring may be replaced Parent 2 X 200 200by 100 the worst 50 250 chromosome 70 250 400 300 200 in Offspring the population 2 if its fitness values 50 400 300 200 300 100 100 300 100 200 X 200 1 200 50 50 300 270 250 200 300 70 exceed theY fitness value of the worst chromosome. Offspring X Y 20050200 Now 400 the 200250 100300250 new 30070100 population 100 180 300 400 100200200 is 100 ready Parent 1 forX a new 200 200 generation. 50 250 300 270 180 200 100 70 Y 50 400 300 200 300 100 200 300 100 200 Y 50 400 300 200 300 100 200 300 100 200 X 200 200 100 50 250 70 250 400 300 200 Offspring 2 Parent 2 Offspring before Y mutation 50 400 300 200 300 100 100 300 100 200 XOffspring 200 200after50 mutation 50 300 270 250 200 300 70 X 200 200 50 50 250 70 250 200 100 70 Y X 50200 3005020050300220 400200 10070100250 300200 10010020070 Y 50 400 300 200 300 100 100 300 100 200 Y 50 400 300 200 300 100 100 300 100 200 0.4 0.7 0.1 0.9 0.6 0.3 0.3 0.5 220 Mutation value = 0.2 Offspring before mutation Offspring after mutation X 200 200 50 50 250 70 250 200 100 70 X 200 200 50 50 220 70 250 200 100 70 Y 50 400 300 200 300 100 100 300 100 200 Y 50 400 300 200 300 100 100 300 100 200 0.4 0.7 0.1 0.9 0.6 0.3 0.3 0.5 Fig. 7. Mutation of x-coordinates of nodes. 0 220 0 Mutation value = 0.2 Level 4 Level 4 8 5 8 5 6 Level 3 Level 3 9 6 9 0 3 Level 2 0 3 Level 2 Level 4 Level 4 2 8 7 5 4 Level 1 2 8 7 5 4 Level 1 6 Level 3 Level 3 9 6 Level 0 9 Level 0 1 1 3 Level 2 3 Level 2 2 7 4 Level 1 2 7 4 Level 1 Level 0 Level 0 1 1 Fig. 8. Mutation of y-coordinate (level) of the node 6. Also we explained how to get a good placing of nodes (concepts) in a graph with a minimal number of intersections. In our approach the main sence of fitness function is to obtain this number. But the function could be modified and other criteria could be added. For example a minimal distance between nodes, a graph symmetry or minimal sum of lengths of graph edges. But this is the point of concrete implementation. 6.4 Termination process The process will be terminated if any of these cases: 1. there will be a chromosome with no intersection which means the best solu- tion, 2. after processing limited number of generations, 3. if there is no improvement over the population in limited number of gener- ations. Usage of Genetic Algorithm for Lattice Drawing 91 7 Conclusion The genetic algorithms are used to solve hard problems, where the drawing of a nice and readable Hasse diagram is one of them. We introduced one possible way to draw it using genetic algorithm. Using different types of the crossover operators may produce god drawing results. Moving nodes from level to level by mutation operator may give us better results. In our future work we will try to create an application for lattice drawing based on our approach. The shape of the graph could be improved using other characteristics like distances between nodes on the same level. References 1. 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