Jawad Ali discusses heuristic algorithms and search methods. A heuristic algorithm sacrifices optimality, accuracy, or completeness for speed in solving problems like NP-complete decision problems. Heuristic search iteratively improves solutions based on a heuristic function or cost measure, finding a good solution quickly without guaranteeing an optimal one. Examples given are swarm intelligence inspired by swarm movements in nature, tabu search preventing repetitive movements, and artificial neural networks inspired by the brain for pattern recognition. Heuristic algorithms have low time complexity and are applied to complex problems.

1. Name: Jawad Ali
Roll no:BSCS-F19-214
Semester: 5

2. Heuristic Algorithm

3. What Is Heuristic algorithm?
A heuristic algorithm is one that is designed to solve a problem in a faster and
more efficient fashion than traditional methods by sacrificing optimality, accuracy,
precision, or completeness for speed. Heuristic algorithms often times used to
solve NP-complete problems, a class of decision problems.

4. Heuristic Search
Heuristic search refers to a search strategy that attempts to optimize a problem by
iteratively improving the solution based on a given heuristic function or a cost
measure. A heuristic search method does not always guarantee to find an optimal
or the best solution, but may instead find a good or acceptable solution within a
reasonable amount of time and memory space. Several commonly used heuristic
search methods include hill climbing methods, the best-first search, the A*
algorithm, simulated-annealing, and genetic algorithms

5. For Example:
1- Swarm Intelligence
The swarm algorithms are search algorithms that are inspired
by the movements of the swarms in nature. A lot of
individuals interact with each other to solve a certain
problem.

6. 2. Tabu Search
Solving is prohibited again in the next steps to prevent
repetitive movement during the steps leading up. Thus,
regional research is conducted to investigate solutions to
achieve the best solution.

7. 3.Artificail Neural Networks
Artificial neural networks are very functional models for
pattern recognition and machine learning, which categorize
new patterns from acquired training data. It was inspired by
the neuron function in the animals' brains. Many areas such as
speech analysis, image processing, etc. are used.

8. Conclusion
Usually heuristic algorithms are developed to have low time complexity and
applied to the complex problems. We briefly defined basic traditional and modern
heuristic strategies. Evolutionary algorithms and Support Vector Machines were
considered more comprehensively.Due to their eminent characteristics they
gained a great popularity. Recently appeared research results confirm the fact that
their applications can be significantly enlarged in the future.