Xloptimizer 2015
Author: d | 2025-04-24
xlOptimizer is a generic optimization tool compatible with Microsoft Excel (2025, 365). xlOptimizer implements a host of customizable, state-of-the-art metaheuristic algorithms. xlOptimizer xlOptimizer is a generic optimization tool compatible with Microsoft Excel (2025, 365). xlOptimizer implements a host of customizable, state-of-the-art metaheuristic algorithms. xlOptimizer
xloptimizer.com - What is xlOptimizer?
I.e. the best, is transferred as-is to the next generation.TerminationThis generational process is repeated until a termination condition has been reached. Common terminating conditions are:A solution is found that satisfies some criteriaAn allocated computation budget is reachedThe highest ranking solution's fitness is reaching or has reached a plateau such that successive iterations no longer produce better resultsCombinations of the aboveOptions in xlOptimizer add-inThe following options are available in xlOptimizer add-in. Note that whenever an asterisk (*) is indicated in a text field, this means that the field accepts a formula rather than a certain value. The formula is identical to Microsoft Excel's formulas, without the preceding equal sign '='. Also, the function arguments are always separated by comma ',' while the dot '.' is used as a decimal point. General settingsName: the name of the scenario to be run. It should be descriptive but also concise, as a folder with the same name will be (optionally) created, which will hold the log files for further statistical analysis.Active: select whether the scenario is active or not. If it is active, it will be run in sequence with the other active scenaria. In the opposite case, it will be ignored. This is very helpful when you experiment with settings. Seeds and repetitionsMetaheuristic algorithms are based on random number generators. These are algorithms that produce a different sequence of random numbers for each seed they begin with. A seed is just an integer, and a different seed will produce a different evolution history with a different outcome. Robust metaheuristic algorithms should, on average, perform the same no matter what the seed is.Random number generator: select the random number generator to be used. The Mersenne Twister is default. The following options are available:NumericalRecipes: Numerical Recipes' [3] long period random number generator of L’Ecuyer with Bays-Durham shuffle and added safeguardsSystemRandomSource: Wraps the .NET System.Random to provide thread-safetyCryptoRandomSource: Wraps the .NET RNGCryptoServiceProviderMersenneTwister: Mersenne Twister 19937 generatorXorshift: Multiply-with-carry XOR-shift generatorMcg31m1: Multiplicative congruential generator using a modulus of 2^31-1 and a multiplier of 1132489760Mcg59: Multiplicative congruential generator using a modulus of 2^59 and a multiplier of 13^13WH1982: Wichmann-Hill's 1982 combined multiplicative congruential generatorWH2006: Wichmann-Hill's 2006 combined multiplicative congruential generatorMrg32k3a: 32-bit combined multiple recursive generator with 2 components of order 3Palf: Parallel Additive Lagged Fibonacci generatorRandom repetitions: use this option if you wish the program to select random seeds for every run. Also, select the number of repetitions. If xlOptimizer is a generic optimization tool compatible with Microsoft Excel (2025, 365). xlOptimizer implements a host of customizable, state-of-the-art metaheuristic algorithms. xlOptimizer xlOptimizer is a generic optimization tool compatible with Microsoft Excel (2025, 365). xlOptimizer implements a host of customizable, state-of-the-art metaheuristic algorithms. xlOptimizer IntroductionxlOptimizer add-in implements Standard Genetic Algorithm (SGA). Genetic Algorithms are inspired by natural selection and survival of the fittest and they are considered to be amongst the most reliable and efficient methods for global optimization. They were introduced by John Holland as a means to study adaptive behavior [1]. Nevertheless, they have been largely considered to be function optimizers, able to provide near-optimum results by evolving a small population of candidate solutions. Since then, they have been applied to virtually any kind of optimization problem conceivable.In particular, GA (in fact, almost all Metaheuristic Algorithms) are attractive for two main reasons: first, they rely on “payoff” data, i.e. not derivative data, which is very important for highly non-linear or combinatorial problems; secondly, they possess an inherent capability for massive parallel computing. It is noted, however, that their performance in discovering the actual local or global optimum is limited due to the so-called anytime behavior; the development of the population’s best individual shows rapid progress in the beginning, followed by gradual degradation until the point when evolution practically stops [2]. For this reason, GA are often coupled with a local optimizer which takes over when the progress of the GA degrades. In xlOptimizer add-in, the user can introduce Greedy Ascent Hill Climber (GAHC) [2] as a local optimizer, simply by selecting the appropriate option in the configuration form. This option invokes a certain (configurable) function evaluations in the beginning of each generation. One can also use the outcome of SGA as seed for a Hill Climbing technique.How does it work?The algorithm mimics the process of natural selection. A population of candidate solutions is evolved, generation by generation, using techniques inspired by natural evolution such as selection, mutation and crossover. The evolution starts from a population of randomly generated individuals. In each generation, the fitness of every individual in the population is evaluated. This is a measure of the quality of the solution, and it is depended on the objective function value corresponding to the solution. Next, multiple individuals are stochastically selected from the current population (according to their fitness) to become parents. Their genetic material is recombined, and possibly randomly mutated, to produce offspring and form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level hasComments
I.e. the best, is transferred as-is to the next generation.TerminationThis generational process is repeated until a termination condition has been reached. Common terminating conditions are:A solution is found that satisfies some criteriaAn allocated computation budget is reachedThe highest ranking solution's fitness is reaching or has reached a plateau such that successive iterations no longer produce better resultsCombinations of the aboveOptions in xlOptimizer add-inThe following options are available in xlOptimizer add-in. Note that whenever an asterisk (*) is indicated in a text field, this means that the field accepts a formula rather than a certain value. The formula is identical to Microsoft Excel's formulas, without the preceding equal sign '='. Also, the function arguments are always separated by comma ',' while the dot '.' is used as a decimal point. General settingsName: the name of the scenario to be run. It should be descriptive but also concise, as a folder with the same name will be (optionally) created, which will hold the log files for further statistical analysis.Active: select whether the scenario is active or not. If it is active, it will be run in sequence with the other active scenaria. In the opposite case, it will be ignored. This is very helpful when you experiment with settings. Seeds and repetitionsMetaheuristic algorithms are based on random number generators. These are algorithms that produce a different sequence of random numbers for each seed they begin with. A seed is just an integer, and a different seed will produce a different evolution history with a different outcome. Robust metaheuristic algorithms should, on average, perform the same no matter what the seed is.Random number generator: select the random number generator to be used. The Mersenne Twister is default. The following options are available:NumericalRecipes: Numerical Recipes' [3] long period random number generator of L’Ecuyer with Bays-Durham shuffle and added safeguardsSystemRandomSource: Wraps the .NET System.Random to provide thread-safetyCryptoRandomSource: Wraps the .NET RNGCryptoServiceProviderMersenneTwister: Mersenne Twister 19937 generatorXorshift: Multiply-with-carry XOR-shift generatorMcg31m1: Multiplicative congruential generator using a modulus of 2^31-1 and a multiplier of 1132489760Mcg59: Multiplicative congruential generator using a modulus of 2^59 and a multiplier of 13^13WH1982: Wichmann-Hill's 1982 combined multiplicative congruential generatorWH2006: Wichmann-Hill's 2006 combined multiplicative congruential generatorMrg32k3a: 32-bit combined multiple recursive generator with 2 components of order 3Palf: Parallel Additive Lagged Fibonacci generatorRandom repetitions: use this option if you wish the program to select random seeds for every run. Also, select the number of repetitions. If
2025-04-07IntroductionxlOptimizer add-in implements Standard Genetic Algorithm (SGA). Genetic Algorithms are inspired by natural selection and survival of the fittest and they are considered to be amongst the most reliable and efficient methods for global optimization. They were introduced by John Holland as a means to study adaptive behavior [1]. Nevertheless, they have been largely considered to be function optimizers, able to provide near-optimum results by evolving a small population of candidate solutions. Since then, they have been applied to virtually any kind of optimization problem conceivable.In particular, GA (in fact, almost all Metaheuristic Algorithms) are attractive for two main reasons: first, they rely on “payoff” data, i.e. not derivative data, which is very important for highly non-linear or combinatorial problems; secondly, they possess an inherent capability for massive parallel computing. It is noted, however, that their performance in discovering the actual local or global optimum is limited due to the so-called anytime behavior; the development of the population’s best individual shows rapid progress in the beginning, followed by gradual degradation until the point when evolution practically stops [2]. For this reason, GA are often coupled with a local optimizer which takes over when the progress of the GA degrades. In xlOptimizer add-in, the user can introduce Greedy Ascent Hill Climber (GAHC) [2] as a local optimizer, simply by selecting the appropriate option in the configuration form. This option invokes a certain (configurable) function evaluations in the beginning of each generation. One can also use the outcome of SGA as seed for a Hill Climbing technique.How does it work?The algorithm mimics the process of natural selection. A population of candidate solutions is evolved, generation by generation, using techniques inspired by natural evolution such as selection, mutation and crossover. The evolution starts from a population of randomly generated individuals. In each generation, the fitness of every individual in the population is evaluated. This is a measure of the quality of the solution, and it is depended on the objective function value corresponding to the solution. Next, multiple individuals are stochastically selected from the current population (according to their fitness) to become parents. Their genetic material is recombined, and possibly randomly mutated, to produce offspring and form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has
2025-04-22Issues existed in WebKit. These issues were addressed through improved memory handling.CVE-IDCVE-2015-1152 : AppleCVE-2015-1153 : AppleCVE-2015-3730 : AppleCVE-2015-3731 : AppleCVE-2015-3733 : AppleCVE-2015-3734 : AppleCVE-2015-3735 : AppleCVE-2015-3736 : AppleCVE-2015-3737 : AppleCVE-2015-3738 : AppleCVE-2015-3739 : AppleCVE-2015-3740 : AppleCVE-2015-3741 : AppleCVE-2015-3742 : AppleCVE-2015-3743 : AppleCVE-2015-3744 : AppleCVE-2015-3745 : AppleCVE-2015-3746 : AppleCVE-2015-3747 : AppleCVE-2015-3748 : AppleCVE-2015-3749 : AppleCVE-2015-5789 : AppleCVE-2015-5790 : AppleCVE-2015-5791 : AppleCVE-2015-5792 : AppleCVE-2015-5793 : AppleCVE-2015-5794 : AppleCVE-2015-5795 : AppleCVE-2015-5796 : AppleCVE-2015-5797 : AppleCVE-2015-5798 : AppleCVE-2015-5799 : AppleCVE-2015-5800 : AppleCVE-2015-5801 : AppleCVE-2015-5802 : AppleCVE-2015-5803 : AppleCVE-2015-5804 : AppleCVE-2015-5805CVE-2015-5806 : AppleCVE-2015-5807 : AppleCVE-2015-5808 : Joe VennixCVE-2015-5809 : AppleCVE-2015-5810 : AppleCVE-2015-5811 : AppleCVE-2015-5812 : AppleCVE-2015-5813 : AppleCVE-2015-5814 : AppleCVE-2015-5815 : AppleCVE-2015-5816 : AppleCVE-2015-5817 : AppleCVE-2015-5818 : AppleCVE-2015-5819 : AppleCVE-2015-5821 : AppleCVE-2015-5822 : Mark S. Miller of GoogleCVE-2015-5823 : Apple Software UpdateImpact: An attacker in a privileged network position may be able to obtain encrypted SMB credentialsDescription: A redirection issue existed in the handling of certain network connections. This issue was addressed through improved resource validation.CVE-IDCVE-2015-5920 : Cylance Information about products not manufactured by Apple, or independent websites not controlled or tested by Apple, is provided without recommendation or endorsement. Apple assumes no responsibility with regard to the selection, performance, or use of third-party websites or products. Apple makes no representations regarding third-party website accuracy or reliability. Contact the vendor for additional information. Published Date: November 03, 2023
2025-04-213 2 Friday, November 13, 2015 4 7 7 Thursday, November 12, 2015 4 2 2 Wednesday, November 11, 2015 1 6 5 Tuesday, November 10, 2015 4 1 6 Monday, November 9, 2015 3 1 4 Sunday, November 8, 2015 7 0 7 Saturday, November 7, 2015 1 8 3 Friday, November 6, 2015 3 6 5 Thursday, November 5, 2015 2 2 2 Wednesday, November 4, 2015 7 4 8 Tuesday, November 3, 2015 6 4 7 Monday, November 2, 2015 7 2 8 Sunday, November 1, 2015 8 9 0 Saturday, October 31, 2015 9 5 1 Friday, October 30, 2015 9 3 3 Thursday, October 29, 2015 1 0 5 Wednesday, October 28, 2015 8 6 2 Tuesday, October 27, 2015 1 9 4 Monday, October 26, 2015 6 1 2 Sunday, October 25, 2015 1 7 1 Saturday, October 24, 2015 2 3 2 Friday, October 23, 2015 1 7 6 Thursday, October 22, 2015 5 3 1 Wednesday, October 21, 2015 7 2 8 Tuesday, October 20, 2015 2 3 3 Monday, October 19, 2015 0 1 7 Sunday, October 18, 2015 7 8 1 Saturday, October 17, 2015 7 3 1 Friday, October 16, 2015 6 3 6 Thursday, October 15, 2015 6 4 1 Wednesday, October 14, 2015 8 9 2 Tuesday, October 13, 2015 0 9 9 Monday, October 12, 2015 2 2 6 Sunday, October 11, 2015 9 1 6 Saturday, October 10, 2015 9 7 1 Friday, October 9, 2015 9 8 1 Thursday, October 8, 2015 2 3 1 Wednesday, October 7, 2015 1 2 3 Tuesday, October 6, 2015 1 6 5 Monday, October 5, 2015 5 2 6 Sunday, October 4, 2015 1 1 6 Saturday, October 3, 2015 1 8 0 Friday, October 2, 2015 2 6 3 Thursday, October 1, 2015 4 9 7 Wednesday, September 30, 2015 9 3 7 Tuesday, September 29, 2015 7 3 9 Monday, September 28, 2015 7 4 3 Sunday, September 27, 2015 3 7 2 Saturday, September 26, 2015 4 0 4 Friday, September 25, 2015 8 7 5 Thursday, September 24, 2015 9 7 3 Wednesday, September 23, 2015 8 2 8 Tuesday, September 22, 2015 9 1 3 Monday, September 21, 2015 6 7 6 Sunday, September 20, 2015 7 9 8 Saturday, September 19, 2015 9 1 5 Friday, September 18, 2015 3 8
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