BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Kolkata BEGIN:STANDARD DTSTART:19451014T230000 TZOFFSETFROM:+0630 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20211225T122322Z UID:E7942A9A-C908-4870-9C5E-FA6553DC1EA6 DTSTART;TZID=Asia/Kolkata:20211213T180000 DTEND;TZID=Asia/Kolkata:20211213T190000 DESCRIPTION:CIS Chapter\, IEEE UP Section (CIS11) organized a Lecture Serie s on "Computational Intelligence". The speaker of the lecture was Dr. Braj esh Varshney\, Director\, Kanpur Institute of Technology\, Kanpur\, India. \n\nSpeaker(s): Brajesh Varshney\, \n\nAgenda: \nAbstract of the talk:\n\n Optimization is the process of making something better. Traditional Optimi zation Algorithms include: Single Variable Optimization Algorithms\, Multi variable Optimization Algorithms\, Constrained Optimization Algorithms and Specialized Algorithms (Integer Programming\, Geometric Programming. Non Traditional Optimization Algorithms include: Genetic Algorithms\, Simulate d Annealing\, Neural Network Based Optimization and Optimization of Fuzzy Systems. Genetic Algorithm (GA) technique is considered very powerful in r ecent decades to solve optimization problems. GA mimics the concepts of na tural genetics and natural selection to constitute search and optimization procedures. GAs are computerized search and optimizations algorithms base d on the mechanics of Natural Selection and Natural Genetics. Prof. John H olland and Ann Arbor gave the concept of these Algorithms (Holland\, 1975) . Prof. Goldberg\, Prof. Deb\, etc. are pioneer in this field. The most im portant characteristic of GA: Robustness. Solutions of GAs are very close to the natural formulation. First stage of GA is the generation of populat ion and evaluation of the chromosome (fitness function). There is lot of f itness function available in the literature. In second stage\, GA operatio ns are carried out. For this there are three GA operators: Reproduction\, Crossover and Mutation.\nReproduction: Good chromosomes from the populatio n are selected and a mating pool is formed. Above average chromosomes are picked from the current population and their multiple copies are inserted in the mating pool. The commonly used reproduction operator is the proport ionate reproduction operator where the chromosome is selected for the mati ng pool with a probability proportional to its fitness. A roulette-wheel s trategy is used.\nCrossover: New strings are created by exchanging the inf ormation among the strings of mating pool. In most cross over operators\, two strings are picked from the mating pool at random and some portions of the strings are exchanged between the strings. There is number of cross o ver operators in GA literature.\nMutation: A crossover operator is mainly responsible for the search of new chromosomes\, even though a mutation ope rator is also used for this purpose. The need for mutation is to create a point in the neighborhood of the current point thereby achieving a local s earch around the current solution. The mutation is also used to maintain d iversity in the population. The random substitution method for mutation op eration is used. That is chromosome chosen to mutate is replaced randomly by a new chromosome with certain mutation probability.\nThese operators wi ll create new population. New population is further evaluated till termina tion criteria is met (i.e. fitness value is keep on increasing).\n\nKanpur \, Uttar Pradesh\, India\, 208016\, Virtual: https://events.vtools.ieee.or g/m/296683 LOCATION:Kanpur\, Uttar Pradesh\, India\, 208016\, Virtual: https://events. vtools.ieee.org/m/296683 ORGANIZER:vrij@iiita.ac.in SEQUENCE:1 SUMMARY:Expert Lecture on "UNDERSTANDING OF GENETIC ALGORITHM" URL;VALUE=URI:https://events.vtools.ieee.org/m/296683 X-ALT-DESC:Description: <br /><p>CIS Chapter\, IEEE UP Section (CIS11) orga nized a Lecture Series on "Computational Intelligence". The speaker of the lecture was Dr. Brajesh Varshney\, Director\, Kanpur Institute of Technol ogy\, Kanpur\, India.</p><br /><br />Agenda: <br /><h4><strong>Abstract of the talk:</strong></h4>\n<p align="justify">Optimization is the process o f making something better. Traditional Optimization Algorithms include: Si ngle Variable Optimization Algorithms\, Multivariable Optimization Algorit hms\, Constrained Optimization Algorithms and Specialized Algorithms (Inte ger Programming\, Geometric Programming. Non Traditional Optimization Algo rithms include: Genetic Algorithms\, Simulated Annealing\, Neural Network Based Optimization and Optimization of Fuzzy Systems. Genetic Algorithm (G A) technique is considered very powerful in recent decades to solve optimi zation problems. GA mimics the concepts of natural genetics and natural se lection to constitute search and optimization procedures. GAs are computer ized search and optimizations algorithms based on the mechanics of Natural Selection and Natural Genetics. Prof. John Holland and Ann Arbor gave the concept of these Algorithms (Holland\, 1975). Prof. Goldberg\, Prof. Deb\ , etc. are pioneer in this field. The most important characteristic of GA: Robustness. Solutions of GAs are very close to the natural formulation. F irst stage of GA is the generation of population and evaluation of the chr omosome (fitness function). There is lot of fitness function available in the literature. In second stage\, GA operations are carried out. For this there are three GA operators: Reproduction\, Crossover and Mutation.<br /> Reproduction: Good chromosomes from the population are selected and a mati ng pool is formed. Above average chromosomes are picked from the current p opulation and their multiple copies are inserted in the mating pool. The c ommonly used reproduction operator is the proportionate reproduction opera tor where the chromosome is selected for the mating pool with a probabilit y proportional to its fitness. A roulette-wheel strategy is used.<br />Cro ssover: New strings are created by exchanging the information among the st rings of mating pool. In most cross over operators\, two strings are picke d from the mating pool at random and some portions of the strings are exch anged between the strings. There is number of cross over operators in GA l iterature.<br />Mutation: A crossover operator is mainly responsible for t he search of new chromosomes\, even though a mutation operator is also use d for this purpose. The need for mutation is to create a point in the neig hborhood of the current point thereby achieving a local search around the current solution. The mutation is also used to maintain diversity in the p opulation. The random substitution method for mutation operation is used. That is chromosome chosen to mutate is replaced randomly by a new chromoso me with certain mutation probability.<br />These operators will create new population. New population is further evaluated till termination criteria is met (i.e. fitness value is keep on increasing).</p> END:VEVENT END:VCALENDAR