Refining and Optimising Algorithms

This lesson covers how to refine and optimise algorithms as required by OCR J277 Section 2.4. Refining means improving an algorithm to make it more correct, clear, or robust. Optimising means making it more efficient — faster, using less memory, or both. These are important skills for producing high-quality, robust programs.

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What Is Refinement?

Refinement is the process of improving an algorithm or program. This might involve:

• Fixing errors discovered during testing
• Adding validation to handle unexpected inputs
• Improving clarity so the code is easier to understand
• Adding features that were missing from the initial version
• Removing redundancy — eliminating unnecessary code

Refinement is iterative — you make improvements, test them, and then make further improvements.

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What Is Optimisation?

Optimisation is the process of making an algorithm more efficient while maintaining the same correct output. Efficiency can be measured in terms of:

Measure	Description
Time efficiency	How quickly the algorithm completes — fewer steps or comparisons
Space efficiency	How much memory the algorithm uses

OCR Exam Tip: When asked to optimise an algorithm, focus on reducing unnecessary operations. Common optimisations include: removing redundant comparisons, using better algorithms (e.g. binary search instead of linear search), and eliminating unnecessary variables or loops.

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Example 1: Removing Redundant Code

Before refinement:

total = 0
count = 0
for i = 0 to 9
    score = int(input("Enter score: "))
    total = total + score
    count = count + 1
next i
average = total / count
print("Average: " + str(average))

After refinement:

total = 0
for i = 0 to 9
    score = int(input("Enter score: "))
    total = total + score
next i
average = total / 10
print("Average: " + str(average))

What changed: The count variable is unnecessary because the loop always runs exactly 10 times. Removing it simplifies the code without changing the output.

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Example 2: Optimising a Search

Before optimisation — linear search that does not stop when the item is found:

names = ["Alice", "Bob", "Charlie", "Diana", "Eve"]
target = input("Enter name: ")
found = false

for i = 0 to names.length - 1
    if names[i] == target then
        found = true
        position = i
    endif
next i

if found == true then
    print("Found at index " + str(position))
else
    print("Not found")
endif

After optimisation — stops searching as soon as the item is found:

names = ["Alice", "Bob", "Charlie", "Diana", "Eve"]
target = input("Enter name: ")
found = false
i = 0

while i < names.length AND found == false
    if names[i] == target then
        found = true
        position = i
    endif
    i = i + 1
endwhile

if found == true then
    print("Found at index " + str(position))
else
    print("Not found")
endif

What changed: The for loop continues checking all elements even after finding the target. The while loop stops as soon as the item is found, saving unnecessary comparisons.

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Example 3: Adding Validation (Refinement)

Before refinement — no input validation: