Learn about the Confusion Matrix with Real-Life Examples. A confusion matrix is a table that shows how well an AI model makes predictions. It compares the actual results with the predicted ones and tells which are right or wrong. It includes True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN). Video Chapters: Confusion Matrix in Artificial Intelligence 00:00 Introduction 00:12 Confusion Matrix 03:48 Metrices Derived from Confusion Matrix 04:26 Confusion Matrix Example 1 05:44 Confusion Matrix Example 2 08:10 Confusion Matrix Real-Life Uses #artificialintelligence #machinelearning #confusionmatrix #algorithm #optimization #research #happylearning #algorithms #meta #optimizationtechniques #swarmintelligence #swarm #artificialintelligence #machinelearning
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Grasshopper Optimization Algorithm (G.O.A.) Step-by-Step with Numerical ...
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Grasshopper Optimization Algorithm (G.O.A.)
Grasshoppers are also known as pests. They destroy fields and crop production. Grasshopper lifecycle contains Eggs, Nymph Phases, and Adult Grasshopper. Grasshopper Optimization Algorithm is a Nature-inspired swarm-based optimization algorithm. Grasshopper Optimization Algorithm (GOA) is inspired by the foraging and swarming behavior of grasshoppers in nature. The grasshopper optimization algorithm is basically inspired by the behavior of adult grasshoppers in nature. Adult grasshoppers can make sudden jumps and cover long-range as compare to nymphs.
This is the mathematical model used to represent grasshopper behavior in this algorithm :
𝑥_𝑖 = 𝑆_𝑖 + 𝐺_𝑖 + 𝐴_i
GrasshopperCurrentPosition = Social Interaction in the group + Force of gravity + Wind Direction.
Normally distributed random values are used in the grasshopper optimization algorithm for grasshopper random behavior in nature.
Grasshopper Optimization Algorithm Steps.
1.) Parameter Initialization.
2.) Population Initialization Phase.
3.) Compute Fitness Value for each grasshopper.
4.) Select the Best Solution Among All.
5.) Check While (CurrentIteration (t) < MaximumIteration (MaxT)).
6.) Normalize distance between grasshoppers in the range [1, 4].
7.) Update the position of the current grasshopper.
8.) Bring the Current grasshopper back if it goes outside boundaries.
9.) Update Current Best Solution if there any new Best solution.
10.) CurrentIteration = CurrentIteration + 1; // End While Loop
11.) Return Best Solution.
Grasshopper Optimization Algorithm Advantages.
Obtain better solution as compare to other metaheuristic algorithms
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